At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here.

Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase.

Coinbase's Developer Infrastructure - Test team exists for one reason: every Coinbase engineer should get fast, reliable test signals so the whole company tests and ships faster. We own test execution at scale , smart test selection, parallelization and sharding, flaky-test detection and remediation, and the observability layer that turns our test suite into a speed advantage rather than a bottleneck. As AI accelerates the rate of code being written, test infrastructure is becoming the critical path for how quickly Coinbase can deliver value to customers. Slow or flaky tests don't just waste an engineer's afternoon , they slow down the entire company.

We're hiring a Staff Software Engineer to join Developer Infrastructure - Test , someone obsessed with speed and reliability who can set the technical direction for how Coinbase tests and ships software at scale, and own it end to end.

What you’ll be doing (ie. job duties):

• Define and own the technical strategy for test infrastructure across all of Coinbase engineering , with speed of feedback as the primary design constraint.
• Build and operate core Developer Infrastructure - Test services: test execution orchestration, smart test selection, parallel sharding, flaky-test detection and suppression, and test result storage and analysis.
• Relentlessly reduce the time between "code written" and "test results in hand" , every minute saved on test feedback compounds across thousands of engineers and PRs per day.
• Drive flakiness rate down so that test signals are trustworthy by default and engineers never waste time re-running or second-guessing results.
• Own your systems end to end , SLOs, observability, on-call , with zero tolerance for correctness issues.
• Partner closely with engineering teams across Coinbase (your customers) to understand where test infrastructure is slowing them down and fix it.
• Mentor engineers across Developer Infrastructure - Test and DevInfra, set technical standards that scale beyond your direct work, and shape how the org approaches test infrastructure problems.

What we look for in you:

• 10+ years building and operating production software, with strong fundamentals in distributed systems and a primary language like Go (or similar).
• Demonstrated track record of defining and delivering technical strategy for foundational systems , not just shipping features, but setting direction that other teams depend on.
• Deep, hands-on experience in test infrastructure: test execution at scale, flaky-test detection, test selection, sharding, or test result analysis.
• Strong operational instincts , you care about reliability, security, and observability, and your systems reflect that.
• A track record of driving complex, cross-team technical projects to completion , influencing direction and timelines without needing direct authority over the people involved.
• A customer-focused mindset: you measure success by the impact your systems have on the engineers who depend on them.
• Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human‑in‑the‑loop practices to deliver business‑ready outputs and drive measurable improvements in efficiency, cost, and quality.

Nice to haves:

• Familiarity with test selection algorithms (change-based, dependency graph, ML-assisted) and the tradeoffs between coverage and speed.
• Experience operating at scale with Kubernetes, AWS, GitHub Actions, Terraform, and containers (Docker/OCI).
• Experience with observability and performance tooling (e.g., Datadog) to measure and improve system behavior , particularly for distributed test execution.
• Curiosity about crypto/web3 (deep prior knowledge not required); experience in a regulated or security-sensitive environment is a plus.

#PID - P76990

Pay Transparency Notice: Base salary varies by location (see range below). Total compensation may also include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)). Annual base salary range (excluding equity and bonus): $218,025-$256,500 USD

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Your work will define how this asset is deployed, sustained, and utilized by our partners in the field. You will work closely with Anduril’s Strategy team and strategic opportunity customers to develop this approach, to ensure that requirements are tied to customer inputs and there is traceability to program office technical decisions.

Your spectrum of support will be diverse in nature and require you to leverage your technical expertise as well as leadership skills to set objectives, build cross-functional teams, and rapidly drive to completion. The ability to leverage your intuition and prior experience in programmatic level decision will play a key component in making sure the right design/analysis/ and test steps are being completed to ensure success.

Specific responsibilities include:

• Plan, coordinate, and lead the development and sustainment of a holistic Support System (including production, launch, depot and field maintenance, training, and everything in between)

• Hire, develop, and grow a multi-disciplinary Support System team (Technicians, Engineers, Technical Writers, Trainers etc) to provide world-class training, logistics, and support

• Collaborate with customer stakeholders on a comprehensive concept of employment to ensure needs are met and customer inputs are used to influence the design of other systems to align with expected methods for employment

• Manage a cohesive plan to support deployed assets, inclusive of identifying critical infrastructure requirements such as maintenance depots, test sites, etc.

• Work directly with operators to gain hands-on experience using our equipment, and assist in the scoping and design of future support infrastructure

• Develop a strategy for customer training and train-the-trainer curriculum, including planning approach to training in a modern tech product environment

• Manage requirements for fielded system support equipment, including handling equipment, shipping and logistics equipment, shoreside C2 systems, host vessels, and remote tasking and monitoring equipment

• Own critical interfaces with other system elements and work closely within the team to deliver a tightly integrated end-to-end system capability

• Own the end-to-end vendor management process, from flowing down technical requirements to ensuring on-time delivery of critical components to meet program deadlines.

Required qualifications include:

• Bachelor’s degree in Systems Engineering, Marine Engineering, Electrical Engineering or Mechanical Engineering, or proven experience in a relevant field

• Experience working in logistics, support, maintenance, or training management roles and familiarity with support of fielded systems in the control of customers

• Experience working on and in service of maritime craft, either commercial or defense focused, and an innate understanding of the requirements to support a robotic systems at sea

• Be an experienced leader with the depth and breadth of leadership capability who enables and empowers their people

• Be able to think strategically and plan long term but also to be in the detail, act tactically when required and to simplify the complex. Strong bias to action and an affective decision maker who is able to operate in a fast paced environment

• Be personally resilient and self-starting; ability to build from the ground up in a highly dynamic and innovative environment

• Be highly collaborative with the ability to work with both internal and external stakeholders, challenging constructively where necessary

• Exercise extremely high ownership; take significant pride in the quality and efficacy of your work

• Ability to obtain and maintain a DOD Secret clearance

• Travel 25% to Customer and Test Sites

Preferred qualifications include:

• Demonstrated understanding of multi-domain unmanned and autonomous weapon system development and employment

• Demonstrated engineering management capability; proven technical and personnel leadership qualities

• Strong understanding of reliability, autonomous system architectures, and overall design approach for unmanned weapons systems

• Experience building and managing cross-functional teams focused around a common vision

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Location: London Heathrow Airport

Shift Pattern: 4 ON 4 OFF, 20 HOURS PER WEEK

Pay Rate: £13.29 per hour

Key Responsibilities:

• To carry out cleaning duties and responsibilities to a high standard as delegated by the Management Team

• To adhere to cleaning protocol and procedures

• Adherence to the health and safety regulations

• Be professional in appearance and manners towards co-workers, clients and any other person within the airport and work location

• Carry out other duties as assigned

• Must have 5 years of checkable history

• All applicants must have the right to work in the UK

Required Skills and Experience:

• Very good attention to details and excellent observational skills

• Being able to work under pressure and multitask

• Be a great team player

• Being able to quickly adapt to a very busy operational environment

• Able to work without constant supervision and follow the management instructions

• Be reliable and flexible and used to working to deadlines

Behaviours:

• Good communication & interpersonal skills

• Flexibility

• Innovative

• Achievement orientated

• Energy/Drive

Benefits:

• 24/7 GP: Both you and your immediate family can speak to a UK-based GP from the comfort of your own home

• Mental Health support and Life Event Counseling

• Get Fit Programme

• Financial and legal support

• Cycle to work scheme

• Access Perks at Work, our innovative employee app where you can find:

• Perks: discounts, gift cards, cashback, and exclusive offers

• Life: Search for resources and tools on topics ranging from family and life to health, money and work

• Support: Online chat or telephone service for urgent support in a crisis

About Us:

ABM is one of the world's largest providers of integrated facility services. A driving force for a cleaner, healthier, and more sustainable world, ABM provides essential services that improve the spaces and places that matter most.

ABM are committed to employment practices that promote diversity and inclusion in employment regardless of age, disability, gender reassignment, sex, marriage and civil partnership status, pregnancy and maternity status, race, religion or belief. We are proud members of the Armed Forces Covenant Employer Recognition Scheme.

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At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here.

Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase.

We're hiring a Staff Software Engineer to join the Payments team within the Platform organization. The Payments team builds and operates the infrastructure, workflows, and APIs that power fiat on- and off-ramps for individual and institutional customers, processing billions of dollars in transaction volume across a growing set of global markets.

You'll own the architecture and technical direction of these systems, ensuring every payment is reliable, scalable, and correct as Coinbase expands into new regions and payment methods.

Responsibilities

• Own the multi-year technical strategy and architecture for the Payments platform, defining the roadmap for scaling integrations, routing, and funds-flow systems across global markets.
• Build and evolve distributed systems that process high-volume payment traffic, designing for fault isolation, graceful degradation, observability, and clear SLOs for Tier-0/Tier-1 services.
• Drive engineering quality by establishing paved roads, shared frameworks, and coding standards that elevate the technical bar across the Payments team and adjacent orgs.
• Partner cross-functionally with Product, Risk, Compliance, and Operations to translate ambiguous business requirements into well-scoped technical solutions that balance speed, safety, and regulatory needs.
• Lead high-stakes scaling and reliability initiatives, anticipating bottlenecks before they occur and driving systematic improvements from incident postmortems.
• Strengthen team capabilities through hands-on mentorship, design reviews, and architectural guidance that accelerate the growth of engineers across the team.

Requirements

• 8+ years of experience in software engineering, including hands-on experience building payment systems, financial infrastructure, or high-volume transactional platforms.
• Demonstrated track record of architecting and delivering fault-tolerant, highly reliable distributed systems at scale, with experience processing billions of dollars in transaction volume.
• Led platform-building efforts that transformed fragmented services into coherent, reusable systems (e.g., shared frameworks, unified APIs, multi-tenant architectures).
• Proven experience owning production reliability, leading high-severity incident response, and driving systemic improvements across multiple teams or domains.
• History of influencing technical direction across teams and organizations without direct authority, including representing your area in org-level architecture and strategy forums.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Pay Transparency Notice

Base salary varies by location (see range below). Total compensation may also include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)). Annual base salary range (excluding equity and bonus): $218,025-$256,500 USD

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Our mission is to democratize finance for all.

An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history.

If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact.

Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers.

We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Advisor Platform team builds systems that help financial advisors serve their clients through scalable, reliable technology. The team develops backend infrastructure that supports advanced financial products, with a focus on availability, performance, and secure data handling.

You’ll work closely with product managers and data teams to deliver systems that support new customer experiences and expand into additional product areas.

The team values thoughtful system design, measurable outcomes, and continuous improvement while helping shape the future of advisory services!

As an Engineering Manager, you will lead engineers responsible for designing and delivering backend systems that operate at scale.

You will guide architectural decisions, support engineers in solving complex technical challenges, and ensure systems meet reliability and performance expectations.

You will partner with product managers, legal, compliance, and operations teams to align technical execution with business priorities and customer needs.

In addition, you will support career development, team effectiveness, and consistent delivery across key initiatives.

This role is based in our Menlo Park, CA and New York, NY offices, with in-person attendance expected at least 5 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community.

Our office experience is intentional, energizing, and designed to fully support high-performing teams.

What you’ll do

• Lead, mentor, and support the growth of a team of software engineers while fostering an inclusive and collaborative engineering culture.

• Define and execute the technical roadmap for brokerage services, balancing platform improvements with delivery of new capabilities.

• Work with product managers, legal, compliance, and operations teams to design reliable financial workflows and customer-facing systems.

• Establish operational metrics, architectural standards, and monitoring practices that improve system reliability, performance, and availability.

• Contribute to organizational planning, resource allocation, and technical direction across the Brokerage Engineering organization.

What you bring

• You have 5+ years of software engineering experience, including experience managing engineers or engineering teams.

• You have experience designing, building, and operating distributed systems with a focus on reliability and performance.

• You have delivered complex technical initiatives from planning through execution.

• You communicate effectively with both technical and business partners.

• You support team development through coaching, feedback, and structured growth planning.

Leadership expectations

Our ambitious roadmap requires a great culture shaped by exceptional leaders.

Here’s what we expect from them:

• Drive high performance by setting clear, focused goals, giving real-time feedback, stretching top talent, and scaling impact through focus, innovation, and tech.

• Hire and retain top talent by setting a high bar, hiring only those who raise it, investing in onboarding, and addressing talent issues quickly and fairly.

• Create community by connecting work to purpose, removing friction while prioritizing safety, building trust and inclusion, and leading from the front with integrity.

What we offer

• Challenging, high-impact work to grow your career.

• Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching.

• Best-in-class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents.

• Lifestyle wallet , a highly flexible benefits spending account for wellness, learning, and more.

• Employer-paid life & disability insurance, fertility benefits, and mental health benefits.

• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!

• Exceptional office experience with catered meals, events, and comfortable workspaces.

In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits.

Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands.

The expected base pay range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones.

For other locations not listed, compensation can be discussed with your recruiter during the interview process.

Base Pay Range:

Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC)

$230,000-$270,000 USD

Zone 2 (Denver, CO; Westlake, TX; Chicago, IL)

$203,000-$238,000 USD

Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL)

$180,000-$211,000 USD

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We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

Training and customizing state-of-the-art AI models is one of the most demanding workloads in computing, and it sits at the heart of Databricks' Mosaic AI mission.

AI Runtime (AIR) is our managed platform for large-scale GPU training and fine-tuning. It gives customers on-demand access to fleets of the latest accelerators and a serverless experience that hides the complexity of provisioning, scheduling, and orchestrating multi-node jobs, with the resilience to keep training running for days or weeks across thousands of GPUs.

AIR powers the full spectrum of custom training, from fine-tuning open models to pre-training frontier-scale foundation models, for some of the most sophisticated AI teams in the world.

As a Senior Software Engineer for AI Runtime, you will play a critical role in building and scaling the systems that make large-scale training fast, reliable, and effortless.

You will drive the architecture and evolution of the managed GPU training stack, spanning scheduling and capacity, distributed training performance, fault tolerance, and the developer experience of launching and operating jobs at scale.

Beyond hands-on contributions to core systems, you will help shape the technical direction for AIR, mentor other engineers, partner across product, research, and platform teams, and contribute to the initiatives that expand the technical and business impact of custom training at Databricks.

The impact you will have:

• Drive the architecture and evolution of AIR's managed GPU training platform, delivering scalable, high-throughput, and resilient training across fleets that span thousands of accelerators.

• Solve the hardest problems in large-scale training, including multi-node orchestration, distributed parallelism strategies, GPU scheduling and dynamic routing, high-throughput data loading, and checkpoint and restore for very long-running jobs.

• Push GPU efficiency and training performance, raising utilization (such as model FLOPs utilization and end-to-end throughput) and lowering cost per training run across diverse model architectures and hardware generations.

• Build the resilience and observability foundations that keep multi-node jobs healthy, detecting and recovering from hardware and software failures with minimal disruption to customers.

• Partner with product, research, and platform teams to shape the APIs, CLI, and developer experience that make it easy to launch, monitor, and debug production training jobs.

• Lead end-to-end engineering efforts, from design through production rollout, holding a high bar for performance, correctness, and reliability.

• Make direct, high-impact contributions to the core systems behind AIR, and help bring up support for the latest accelerators and new regions as the fleet grows.

• Champion engineering excellence, mentor other engineers through design reviews and technical discussions, and contribute to Databricks' technical direction in AI training infrastructure.

What we look for:

• 5+ years of experience building and operating large-scale distributed systems, with experience in GPU training infrastructure, high-performance computing, or ML systems.

• Experience with distributed training frameworks (such as PyTorch, FSDP, DeepSpeed, or Megatron) and the parallelism strategies (data, tensor, pipeline, and sequence parallelism) used to train large models.

• Strong understanding of training resilience patterns, including checkpointing, failure detection, and automatic recovery for long-running, multi-node jobs.

• Solid grasp of GPU performance fundamentals, including accelerator architecture, high-speed interconnects (such as NVLink and InfiniBand or RoCE), collective communication, and the bottlenecks that govern training throughput and utilization.

• Experience building and operating managed, multi-tenant platform products in the cloud, with clear SLAs and SLOs for availability, performance, and reliability.

• Strong foundation in algorithms, data structures, and system design as applied to performance-sensitive, large-scale distributed systems.

• Proven ability to deliver technically complex, high-impact initiatives that create clear customer or business value.

• Strong communication skills and the ability to collaborate across product, research, and infrastructure teams in a fast-moving environment.

• Customer-focused mindset with the ability to align implementation details with product goals, and a passion for mentoring engineers and fostering technical excellence.

• BS in Computer Science or a related field (MS or PhD preferred).

Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here. Local Pay Range $160,000-$225,000 USD

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This leader will define how Shield AI drives aircraft availability, maintenance execution, reliability improvement, technical data, spares, repairs, field support, and customer sustainment outcomes. The role will partner closely with all support departments to ensure product support is operationally effective, financially sustainable, and ready to scale across domestic and international customers.

Responsibilities:

• Stand up and lead the product support operating model for an existing UAS aircraft program, including sustainment processes, governance, roles, metrics, and execution rhythms.
• Define the integrated product support strategy across maintenance, reliability, technical data, spares, repairs, support equipment, training, field support, and customer support operations.
• Own sustainment performance metrics including aircraft availability, mission readiness, maintenance burden, MTTR, MTBF, fault recurrence, turnaround time, material consumption, repair cycle time, and operating margin.
• Develop scalable product support processes for Contractor Logistics Support, Performance-Based Logistics, spares provisioning, repair flow, depot/vendor support, field support, and global sustainment operations.
• Drive maintenance concept development across preventive, corrective, condition-based, and reliability-centered maintenance approaches.
• Lead sustainment analytics and readiness improvement efforts by identifying key drivers of downtime, repeat failures, maintenance burden, supply constraints, and readiness degradation.
• Own the technical data and maintenance information ecosystem, including accuracy, usability, configuration alignment, field feedback, IETM/IPB inputs, and digital workflow integration.
• Establish engineering feedback loops that translate field issues, maintenance findings, reliability trends, and customer support needs into dispositions, corrective actions, product improvements, and system-level design changes.

Required qualifications:

• Bachelor’s degree in engineering, aviation, business, finance, logistics, or a related field.
• 10+ years of experience in aerospace and defense sustainment, aviation product support, aircraft maintenance, logistics, reliability, or fleet operations.
• Experience leading or building sustainment, logistics, maintenance, repair, spares, technical data, or product support functions for fielded aircraft or complex aerospace systems.
• Strong understanding of aircraft sustainment drivers, including maintenance operations, readiness, reliability, failure modes, forecasting, supply chain, lifecycle support, and customer support performance.
• Demonstrated ability to translate operational, maintenance, reliability, logistics, and financial data into actionable product support decisions.
• Experience working cross-functionally across engineering, operations, supply chain, finance, program management, and customer-facing organizations.
• Experience supporting proposal development, pricing inputs, Basis of Estimate development, contract execution, or customer sustainment planning.
• Ability to operate in ambiguous, fast-moving environments and build scalable systems from early or immature support concepts.
• U.S. Citizenship required.
• Active Secret clearance required.

Preferred qualifications:

• Experience supporting UAS, autonomous aircraft, advanced aerospace systems, or defense aviation programs.
• Experience with Integrated Product Support, Contractor Logistics Support, Performance-Based Logistics, or lifecycle sustainment planning.
• Background in aircraft maintenance management, reliability engineering, maintainability analysis, fleet sustainment, or technical data systems.
• Experience with technical publications, IETM, IPB, configuration-driven maintenance, or digital maintenance workflows.
• Experience with MRO, ERP, logistics, fleet management, reliability analytics, or maintenance management systems.
• Experience improving sustainment cost structure, spares planning, repair flow, vendor performance, or operational efficiency.
• Prior experience leading cross-functional teams or standing up new support organizations for fielded aircraft programs.

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The ideal candidate will be a self-starter who thrives in dynamic environments, possesses a deep understanding of complex military systems, and can drive innovation in product support and sustainment strategies. Additionally, the candidate will partner closely with environmental health and safety teams to guarantee that our products meet the highest safety standards.

The Logistics Engineer will perform analyses and develop customer reports using software modeling tools such as COMPASS, Power Log-J, SLICWave, and Relyence. The candidate will also partner with technical writers by sourcing data, knowledge, drawings, and guidance to support and manage the creation and updates of Technical, Operations, and Maintenance Manuals as well as rework instructions and troubleshooting guides.

The successful candidate will have 5+ years of experience in Integrated Product Support or Logistics Engineering role and will be proficient in MS Office Suite and Integrated Product Support development models. The candidate will also have good working knowledge of Reliability, Maintainability, and Product Safety to ensure emphasis is placed on designing for high levels of supportability and availability.

The salary range for this role is $129,000-$171,000 USD per year.

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The sustainment of an autonomous undersea fleet is not the sustainment of a crewed grey-hull. There is no ship's company at sea, no decade-long PBL written against a stable baseline, and no resident maintainers. Vehicles are software-defined, telemetered, and measured in mission cycles, not steaming days. We are looking for an engineering leader who sees that clearly and can architect something better, not retrofit a legacy model.

As a Director, XL-AUV Support Systems, you will be an engineer with genuine commercial acumen. Technically credible enough to pressure-test our reliability and supportability decisions, and commercially fluent enough to shape outcomes-based agreements with the Customer. You will speak the language of the Customer but move at Anduril's pace.

Key responsibilities include:

• Defining and driving the long-term strategy and roadmap for XL-AUV sustainment, aligned with program delivery milestones.
• Architecting a support model for autonomous undersea fleets that is data-driven and articulating it to engineers, executives, and the Commonwealth.
• Owning the ILS deliverables across the product line: R&M targets, FMECA, RCM, sparing, support and test equipment, technical publications, training, and facilities.
• Driving supportability into the design through close partnership with engineering: early, often, and with data.
• Standing up and running the support depot and turnaround operation that recovers, services, and re-deploys vehicles between sorties.
• Owning vehicle health analytics, parts flow, configuration management, and end-to-end fleet readiness.
• Being the senior Anduril engineering voice to RAN and NSSG on all matters of in-service support.
• Shaping the support contract construct within the ASDEFCON environment, partnering with Contracts and Programs to negotiate outcomes-based agreements.
• Hiring, developing, and growing a multidisciplinary team of sustainment engineers, ILS specialists, depot operators, fleet analysts, and customer-facing program managers.
• Partnering across Engineering, Production, Supply Chain, Programs, Contracts, and T&E to ensure support is built in from the outset, not bolted on at the end.

Requirements include:

• A Bachelor's degree in Engineering or a related technical discipline.
• 10+ years of progressive experience in defence engineering and sustainment, with a track record of delivering capability and outcomes.
• Demonstrated experience designing or operating support systems for complex, hardware-intensive, software-enabled defence platforms.
• Working fluency with the ASDEFCON environment, with the ability to read an SOW and instinctively know where it will fail.
• Strong commercial acumen. Comfortable shaping a support agreement, modelling a sustainment business case, and explaining cost-of-ownership to an executive sponsor.
• Proven ability to speak Commonwealth and move at Anduril pace.
• Experience building or scaling a function from a small team, not just operating a mature one.
• Strong bias for action and extremely high ownership in a fast-paced environment.
• Resident in Australia.
• Australian citizen, eligible to obtain and maintain an NV2 security clearance.

Preferred qualifications include:

• Direct experience with the sustainment of unmanned or autonomous systems (subsea, surface, air, or land).
• Background as a Royal Australian Navy or allied engineering officer (MEO / WEEO / submarine engineering).
• Prior leadership of an in-service support function at a defence prime, with exposure to performance-based or outcomes-based contracting.
• Subsea oil & gas asset integrity, reliability, or remote-operations experience.
• Aviation fleet sustainment background.
• Familiarity with predictive maintenance, fleet health analytics, and digital sustainment tooling.

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You will collaborate closely with product engineers, security researchers, and customer-facing teams. The work spans fast-moving product development and hard systems problems: long-running workflows, large repositories, sensitive data, reliability, observability, and a high bar for earning user trust.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

You will:

• Build end-to-end workflows for vulnerability discovery, security scanning, red teaming, findings review, remediation, and reruns.
• Design and operate backend services for long-running security work, including APIs, asynchronous orchestration, durable state, and integrations with developer workflows.
• Make complex security results actionable through clear product surfaces, strong evidence, thoughtful prioritization, and reliable reporting.
• Partner with security researchers, product teams, and users to evaluate quality, reduce noise, improve coverage, and ship safely.

You might thrive in this role if you:

• Have experience shipping production full-stack products across modern web frontends and backend services.
• Can design clear APIs and data models, reason about asynchronous systems, and diagnose reliability or performance problems.
• Care about building products that experts trust while making sophisticated workflows usable for a broader set of engineers.
• Bring strong judgment around privacy, security, and correctness. Application-security or cybersecurity experience is helpful, but not required.

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The ideal candidate will have 3–5+ years of experience as a Software Engineer and have managed engineers for 2+ years. They should be comfortable in a design review, have built systems other teams depended on, and think clearly about failure modes and treat reliability as a feature.

The expected base pay range for this position is $190,000 - $237,500, based on a variety of factors including qualifications, experience, and geographic location. In addition to base salary, this role may be eligible for an equity grant, depending on the position and level.

Benefits offered include equity compensation, medical, dental, and vision coverage, HSA/FSA, 401K, work-from-home stipend, therapy reimbursement, 16-week parental leave for eligible employees, carrot fertility annual reimbursement and membership, 13 paid holidays each year as well as a holiday break during the week between December 25th and December 31st, flexible PTO, employee assistance program (EAP), training and professional development.

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$220K – $405K • Offers Equity

U.S. Benefits

Full-time U.S. employees enjoy a comprehensive benefits program including equity, health, dental, vision, retirement, fitness, commuter and dependent care accounts, and more.

International Benefits

Full-time employees outside the U.S. enjoy a comprehensive benefits program tailored to their region of residence.

USD salary ranges apply only to U.S.-based positions. International salaries are set based on the local market. Final offer amounts are determined by multiple factors, including experience and expertise, and may vary from the amounts listed above.

About the Role

The Backend Platform team is the backbone of Perplexity’s product reliability, scalability, and performance, enabling every product and AI team to build with confidence.

This high‑impact group builds and maintains critical infrastructure, including backend systems for authentication, authorization, real-time data flows, service orchestration, and sandboxing, to ensure fast, reliable, and secure user experiences. By setting rigorous standards for code quality, uptime and performance, the Platform team ensures everyone at Perplexity can innovate at speed on top of a robust, well-architected foundation.

Key Responsibilities

• Lead the design and implementation of our sandboxing platform to safely execute untrusted and user‑generated code while meeting strict performance and reliability requirements.

• Define and extend our RBAC and authorization primitives, ensuring they scale with new product capabilities and enterprise controls.

• Own key backend services involved in authentication and encryption flows (including BYOK‑related pieces), partnering with Security and Infra on defense‑in‑depth.

• Set the technical bar for backend platform design and operations, driving best practices in reliability, observability, and incident response for always‑on systems.

• Partner with product and AI teams to define clear platform interfaces, SLAs, and integration patterns so new features can reliably build on shared backend capabilities.

• Provide technical leadership across the backend platform area, mentoring other engineers and making long‑term architectural decisions that keep the foundation simple, secure, and evolvable.

Qualifications

• Experience designing and building backend systems that run in production (typically 4+ years for mid-level, more for senior and staff).

• Strong system design skills, with a track record of creating efficient, reliable, and scalable architectures.

• Hands-on experience with modern infrastructure (for example AWS, Kubernetes, and related cloud technologies).

• Strong proficiency in at least one backend language such as Python, Go, or Rust, and the ability to work effectively in a multi-language environment.

• Collaborative mindset and eagerness to solve challenging technical problems with other experienced engineers.

• Comfort working in security‑sensitive areas (auth, authorization, encryption) and making pragmatic trade-offs between safety, simplicity, and velocity.

If you’re excited about this role, we encourage you to apply even if your experience doesn’t match every qualification listed above.

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This role focuses on building distributed data systems and APIs that power adaptive, context-aware experiences across Microsoft AI. We aim to make Copilot feel like your Copilot , responsive to your preferences, workflows, and goals , while preserving privacy, security, performance, and scale.

We are looking for a Principal Software Engineer to lead the design and development of distributed data infrastructure, APIs and personalization pipelines that drive Copilot's intelligence. You will work across Microsoft AI and Copilot teams.

You will possess a methodical approach to problem-solving, proficiency in backend technologies, a familiarity with applied AI and its unique challenges, and the ability to architect solutions that stand the test of time. The right candidate is hands-on and enjoys building world-class consumer experiences and products in a fast-paced environment. A key skill is the judgment to make the right risk vs velocity and value decisions.

Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

• Architect scalable, low-latency systems for ingesting, processing, and serving personalized signals.
• Design data models and APIs that enable Copilot to reason about user context, preferences, and history.
• Build real-time and batch personalization engines that adapt Copilot's behavior.
• Collaborate with privacy, security, and responsible AI teams to ensure personalization is safe, transparent, and user-controlled.
• Optimize for performance, reliability, and cost across diverse workloads and geographies.
• Ship high-quality, well-tested, secure, and maintainable code.
• Find a path to get things done despite roadblocks to get your work into the hands of users quickly and iteratively.
• Enjoy working in a fast-paced, design-driven, product development cycle.
• Embody our Culture and Values.

Qualifications:

• Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.
• OR equivalent experience.

Preferred Qualifications:

• 4+ years' experience building scalable services, including securing applications and infrastructure on top of cloud infrastructure like Azure, AWS, or GCP.
• 3+ years' experience in OSS data technology, such as Kafka, Spark, Flink.
• Experience with large scale data systems.
• Experience working with AI platforms, frameworks, and APIs.
• Experience using Machine Learning frameworks, including experience using, deploying, and scaling language learning models, either personally or professionally.
• Ability to identify, analyze, and resolve complex technical issues, ensuring optimal performance, scalability, and user experience.
• Demonstrated interpersonal skills and ability to work closely with cross-functional teams, including product managers, designers, and other engineers.
• Passion for learning new technologies and staying up to date with industry trends, best practices, and emerging technologies in web, data systems and AI.
• Ability to work in a fast-paced environment, manage multiple priorities, and adapt to changing requirements and deadlines.
• Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team.

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This role focuses on building distributed data systems and APIs that power adaptive, context-aware experiences across Microsoft AI. We aim to make Copilot feel like your Copilot , responsive to your preferences, workflows, and goals , while preserving privacy, security, performance, and scale.

We are looking for a Principal Software Engineer to lead the design and development of distributed data infrastructure, APIs and personalization pipelines that drive Copilot’s intelligence. You will work across Microsoft AI and Copilot teams.

You will possess a methodical approach to problem-solving, proficiency in backend technologies, a familiarity with applied AI and its unique challenges, and the ability to architect solutions that stand the test of time. The right candidate is hands-on and enjoys building world-class consumer experiences and products in a fast-paced environment. A key skill is the judgment to make the right risk vs velocity and value decisions.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

• Architect scalable, low-latency systems for ingesting, processing, and serving personalized signals.
• Design data models and APIs that enable Copilot to reason about user context, preferences, and history
• Build real-time and batch personalization engines that adapt Copilot’s behavior.
• Collaborate with privacy, security, and responsible AI teams to ensure personalization is safe, transparent, and user-controlled
• Optimize for performance, reliability, and cost across diverse workloads and geographies.
• Ship high-quality, well-tested, secure, and maintainable code.
• Find a path to get things done despite roadblocks to get your work into the hands of users quickly and iteratively.
• Enjoy working in a fast-paced, design-driven, product development cycle.
• Embody our Culture and Values.

Qualifications:

• Bachelor’s Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
• OR equivalent experience.

Preferred Qualifications:

• 4+ years’ experience building scalable services, including securing applications and infrastructure on top of cloud infrastructure like Azure, AWS, or GCP.
• 3+ years’ experience in OSS data technology, such as Kafka, Spark, Flink.
• Experience with large scale data systems
• Experience working with AI platforms, frameworks, and APIs.
• Experience using Machine Learning frameworks, including experience using, deploying, and scaling language learning models, either personally or professionally.
• Ability to identify, analyze, and resolve complex technical issues, ensuring optimal performance, scalability, and user experience.
• Demonstrated interpersonal skills and ability to work closely with cross-functional teams, including product managers, designers, and other engineers.
• Passion for learning new technologies and staying up to date with industry trends, best practices, and emerging technologies in web, data systems and AI.
• Ability to work in a fast-paced environment, manage multiple priorities, and adapt to changing requirements and deadlines.
• Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team.

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You will help turn fragmented launch and incident support into durable, reusable, privacy-safe data foundations that Trust & Safety teams can rely on. The systems your team builds will help OpenAI detect risk, investigate abuse, power operational workflows, develop and evaluate safety models, measure interventions, support product launches, and report accurately on platform integrity.

Key responsibilities include:

• Leading and growing a high-performing Trust & Safety Data Engineering team.
• Defining the roadmap and technical strategy for Trust & Safety data systems.
• Building canonical, privacy-safe datasets and pipelines for abuse detection, fraud detection, risk signals, enforcement, scaled review, transparency reporting, and safety monitoring.
• Creating reusable foundations for Trust & Safety model development, including features, labels, training data, backtesting, evaluation, and production inputs.
• Establishing ownership, documentation, data quality standards, monitoring, and operational rigor for critical datasets and workflows.

We are looking for someone with a strong track record of leading data engineering teams, experience in trust and safety, integrity, abuse prevention, fraud, investigations, risk operations, safety systems, privacy, or adjacent domains, and a deep understanding of data architecture, modeling, pipelines, reliability, privacy, and operational tradeoffs.

Nice to have experience supporting ML systems through feature engineering, training data, labels, model evaluation, or production model pipelines, and experience with launch readiness, monitoring, alerting, incident response, semantic layers, metrics governance, or executive-facing reporting.

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As a Staff Software Engineer on the Onboarding team, you will be responsible for defining and accelerating the backend architecture for a newly onboarded user. This includes the signal-collection systems that learn who a new Redditor is, the personalization pipelines that turn those signals into a tailored first feed, and the experimentation infrastructure that lets us iterate on the moments that decide whether someone becomes a lifelong user.

You will also partner with the Engineering Manager to cultivate a growth engineering mindset, scale impact through collaborative engineering, instrument, measure, and iterate, and guide cross-functional alignment. Additionally, you will raise the bar on engineering excellence by advocating for reliability, scalability, and maintainability across a complex, high-scale distributed system.

We are looking for a strategic partner who can steer cross-team projects that solve tangible business problems, rather than just hitting a 'ship' date. You should be a systems thinker who sees how the code, the product, and the team dynamics all influence each other. You should be able to plan for next year while still helping an engineer through a blocker today.

You should be a mentor who helps other engineers level up, and you should be energized by the opportunity to build culture and craft on a team, not just software. You should be deeply data-informed and logical, ensuring metrics are part of the design process, not just an afterthought in a dashboard.

You should be comfortable with growth-team ambiguity, navigating it with confidence when the goal is clear but the path is not. You should be comfortable with growth-team speed,prototyping fast, moving on when the data doesn't back a bet, and keeping the team focused when the plan changes.

You should have a focus on clear, transparent communication, effectively distilling complex technical trade-offs for any audience. You should have experience with ranking, recommendations, and personalization, and you should have a proven track record of working on ML-heavy products.

Benefits include comprehensive healthcare benefits and income replacement programs, 401k with employer match, global benefit programs that fit your lifestyle, family planning support, gender-affirming care, mental health & coaching benefits, flexible vacation & paid volunteer time off, and generous paid parental leave.

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This role bridges capability, engineering, manufacturing, and supply chain,ensuring our designs evolve from prototype to field-ready products with the reliability, quality, and repeatability required for mission success.

The ideal candidate has deep experience in hardware product development and system integration, thrives in ambiguous environments, and loves building systems that ship.

You'll partner with mechanical, electrical, and robotics engineers; drive integration of sensors and electromechanical subsystems; and ensure a smooth transition from engineering to production.

You'll also serve as the primary POC between the product team and internal business lines, balancing customer urgency with disciplined execution.

This isn't a coordination job , it's an opportunity to build and scale real, deployable systems that protect lives and reshape defense operations.

You'll be part of a small, high-ownership team that works at the intersection of engineering execution and product impact, ensuring that cutting-edge autonomous platforms move from the lab to the field , fast, reliable, and at scale.

Key responsibilities include:

• Leading hardware product development across core subsystems and full system integration into high-performance, mission-critical autonomous platforms.

• Partnering with engineering leads to plan, execute, and deliver design, build, and test milestones through production readiness.

• Driving design for manufacturability (DFM/DFA), supplier engagement, and hand-off to manufacturing and supply chain teams.

• Managing prototype builds, integration events, and system-level validation and reliability testing.

• Owning risk management and schedule execution for hardware deliverables; escalating blockers and driving resolution.

• Working directly with production, test, and quality to ensure systems meet reliability, qualification, and sustainment requirements (MIL-STD experience preferred).

• Supporting field deployments and test events to gather feedback, debug issues, and identify improvement opportunities.

• Establishing lightweight, repeatable processes to improve team velocity and predictability without sacrificing agility.

• Communicating status, risks, and decisions to engineering, product, and program stakeholders clearly and confidently.

Required qualifications include:

• 5–10+ years experience driving hardware product development and system integration in robotics, defense, aerospace, automotive, or industrial systems.

• Proven track record taking electromechanical systems from prototype through production and sustainment.

• Strong technical fluency in mechanical, electrical, or systems engineering domains (power systems, sensors, mechatronics, or robotics).

• Deep familiarity with manufacturing processes, DFM/DFA, BOM management, and supplier readiness.

• Experience with qualification, reliability, and environmental testing (e.g., MIL-STD, DO-160, automotive validation).

• Hands-on mindset , comfortable on the build floor or at test events, not just in meetings.

• Exceptional ability to manage cross-functional execution and communicate clearly across disciplines.

• Bias for action; thrives in a startup-like, fast-moving environment with incomplete information.

• Bachelor’s degree in Mechanical, Electrical, Systems, or related Engineering field (advanced degree preferred).

• Eligible to obtain and maintain an active U.S. Secret security clearance.

Preferred qualifications include:

• Background in sensor integration (EO/IR, radar, GPS, or similar).

• Prior work in defense, autonomy, or mission-critical systems.

• Familiarity with hardware-software co-development and system-level integration testing.

• Experience leading small technical teams or mentoring junior TPMs.

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Omada has a dedicated AI Transformation & Enablement organization focused on embedding AI directly into how business functions operate. Rather than acting as a centralized innovation lab disconnected from day-to-day operations, this team deploys senior engineers directly into functions to partner with teams, understand their workflows, and build production AI systems that drive measurable business impact.

Finance is the next major deployment. As the Sr. Forward Deployed Engineer supporting Finance, you will operate as the embedded AI engineering partner for the Finance organization , attending planning reviews, close cycles, forecasting discussions, operational reviews, and executive preparation processes to deeply understand how Finance operates today and how AI can transform it tomorrow.

This is a 0→1 build with a team behind you, focused on turning ambiguous, high-value finance problems into production systems used by executives and operators every day. You will own each agent end-to-end: from discovery and prototyping, to shipping, monitoring, and iterating.

You will partner closely with the Sr. Director, Strategic Finance, who owns the Finance AI roadmap and business prioritization, while remaining part of Omada’s AI Transformation & Enablement engineering organization, where you will receive technical leadership, architecture guidance, peer collaboration, and engineering support from other Forward Deployed Engineers solving similar challenges across the company.

This role is highly cross-functional and combines elements of AI engineering, systems integration, workflow automation, product thinking, business partnership, and organizational enablement. You will design, build, deploy, monitor, and continuously improve AI-powered systems that help Finance teams operate more efficiently, improve accuracy and consistency, accelerate decision-making, and scale institutional knowledge.

The systems you build will operate within a highly regulated and compliance-sensitive environment that includes SOX, HIPAA, security, and data governance considerations. As part of the AI Transformation & Enablement organization, you will help shape the standards, governance patterns, and operational practices that define responsible enterprise AI adoption at Omada.

What You’ll Own:

Finance AI Integration & Knowledge Layer

Design and build the Finance-specific AI integration layer that connects AI systems to enterprise finance platforms, operational data, and institutional knowledge sources. This includes:

• Integrating AI systems with platforms such as NetSuite, Adaptive Planning, FloQast, AWS, and internal data warehouses

• Building retrieval and knowledge systems over financial documentation, board materials, KPI definitions, investor communications, and forecasting models

• Developing reusable AI workflow and orchestration patterns for Finance use cases

• Enabling conversational and natural-language interaction with operational and financial data

• Partnering with central Engineering and AI platform teams to adopt and extend shared AI infrastructure patterns

You will design systems that allow Finance teams to progressively own configuration, evaluation, and operational management of AI workflows over time.

AI-Powered Financial Quality & Controls

Build AI-enabled quality assurance and validation systems that improve confidence, consistency, and operational rigor across Finance workflows. Examples include:

• Metric reconciliation across reporting materials and presentations

• Narrative and KPI consistency validation

• Financial calculation verification

• Reporting integrity and formatting validation

• AI-assisted review workflows with human-in-the-loop oversight

• Evaluation and auditability patterns supporting SOX-aligned processes where applicable

You will help establish evaluation frameworks and review processes that ensure AI systems are reliable, measurable, and operationally trustworthy.

Operational Intelligence & Automation

Develop AI systems that continuously analyze operational and financial processes to surface insights, anomalies, optimization opportunities, and business risks. Areas may include:

• Cost and spend monitoring

• Vendor and procurement analysis

• Contract and compliance monitoring

• Pricing and operational trend analysis

• Cloud and AI platform cost optimization

• Automated summarization, alerting, and root-cause analysis

You will focus on delivering practical AI solutions that improve operational efficiency and support faster, more informed decision-making.

Market, Earnings & Strategic Intelligence

Build AI-powered intelligence capabilities that help Finance leadership monitor external market activity and prepare executive-level materials more efficiently. Examples include:

• Monitoring competitor activity, earnings calls, filings, and analyst commentary

• Supporting benchmarking and market intelligence workflows

• Assisting with earnings preparation, executive Q&A, and board preparation workflows

• Cross-referencing and validating data across financial narratives, trend reporting, and investor-facing materials

Financial Workflow & Narrative Automation

Develop AI-enabled workflows that accelerate operational execution and reduce manual effort across recurring Finance processes. Examples may include:

• Monthly and quarterly reporting commentary

• Flash reports and KPI summaries

• Forecast and enrollment analysis

• Revenue and operational model validation

• Executive memo and board material drafting support

You will partner closely with Finance SMEs to ensure workflows remain operationally accurate, transparent, and trusted.

AI Evaluation, Enablement & Adoption

Enable Finance teams to effectively evaluate, use, and extend AI systems over time. This includes:

• Building evaluation frameworks, regression testing, and quality scorecards

• Establishing operational review and feedback loops

• Training Finance SMEs on AI evaluation and workflow management

• Promoting transparency around AI capabilities, limitations, and reliability

• Helping Finance teams grow long-term AI fluency and operational ownership

Success in this role is not just measured by systems you build, but by how effectively the organization adopts and scales them.

Cross-Functional Partnership & Operating Model:

This role operates with two closely connected partnership models by design. From a business perspective, you will be deeply embedded within the Finance organization and aligned to Finance priorities, workflows, operational rhythms, and strategic initiatives. The Sr. Director, Strategic Finance will own roadmap prioritization and business outcomes for Finance AI initiatives. From a technical perspective, you will remain part of Omada’s AI Transformation & Enablement engineering organization, where you will collaborate with other Forward Deployed Engineers, participate in shared architectural standards, receive technical mentorship and code review, and contribute reusable patterns that scale across other functions.

You will regularly partner across:

• Finance

• Accounting

• IT

• Security & Compliance

• Data & Engineering

• Business Systems

• Enterprise AI & Automation teams

This role requires balancing speed, innovation, governance, operational reliability, and stakeholder alignment in a rapidly evolving AI landscape.

What Makes This Role Different:

• Embedded partnership model: You work directly inside Finance while remaining part of a centralized AI engineering organization

• High ownership environment: You will own initiatives from discovery through deployment and iteration

• Real operational impact: The systems you build will be used daily by Finance operators and leadership teams

• AI-first transformation work: This is not incremental automation , this role helps redefine how Finance workflows operate

• Strong executive sponsorship: You will have direct access to and partnership with the Sr. Director, Strategic Finance, who owns the Finance AI roadmap and business prioritization

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You will think strategically as well as roll up your sleeves and dive deep into practical application. You must understand business priorities and translate them into an HR agenda that supports the business as it grows rapidly. At NVIDIA, the HR Business Partner also serves as a liaison between the business and the central corporate teams to assess the human resources needs in areas such as performance management, employee relations, compensation, career development and leadership development.

Responsibilities:

• Work with assigned BU and senior management teams to understand business strategies, diagnose current organisation performance, and develop/implement plans to build needed organisation capabilities and support bold growth.

• Partner with the business on organisational design, talent management and building management bench strength

• Leading end to end OD & Learning initiative and programs from gap analysis to best practices solutions

• Serve as an advisor and coach to managers and employees on a variety of people issues

• Review and analyse business metrics including data on retention and employee experience to recommend systemic improvements

• Partner with the business to lead annual review processes which focus on performance management and talent development

• Provide compensation support including salary planning, approval of salary actions, promotions and job re-leveling

• Participate in BU leadership team meetings and contribute as a business leader

• Be a key member of the HRBP team helping to develop world-class HR capabilities

• Work with line managers and employees to address various employee issues, fairly representing all interests

• Work with business groups to assess, create and implement innovative solutions for a variety of employee engagement initiatives within the client group

• Drive teamwork within Global HR to provide excellent HR service delivery

Requirements:

• Master's Degree or equivalent experience in Human Resources Management or related field or business preferred

• A combination of HR experience in the following areas: organisational design, change management, building management bench strength, performance management, diversity and inclusion, compensation and rewards, recognition programs, culture champion, employee engagement, coaching and development, talent management, conflict resolution, talent acquisition, data analysis, and employee relations

• Strong business insight with a keen aptitude to quickly assimilate new information

• Demonstrate ability to establish trust, fairness and credibility with employees, managers and other partners in a multicultural environment

• Solid team player and ability to function in a matrixed co-leadership environment

• Excellent consulting and conflict management skills

• Experience working with senior management in an 'influence-without-authority' role. Can confidently express your opinion, based on thoughtful data and analysis

• Ability to confidently express an opinion with thoughtful data and analysis that help make business decisions

Preferred Qualifications:

• Experience handling cross-functional projects and initiatives

• Self-starter with strong results-orientation and strong communication and presentation skills

• Fluency in MS Word, Excel and PowerPoint and HR systems (preferably Workday)

• Sound judgement in handling confidential or sensitive information

• Strong attention to detail and on-time completion of deliverables

• Excellent interpersonal skills, including both written and verbal communications

• Creativity, initiative, and a positive approach to solving business issues

• Consistent track record of reliability and willingness to focus and commit

• Drive for excellence and continuous improvement

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This product sits within ArsenalOS Quality and will start by connecting supplier quality workflows into a seamless operating environment , replacing fragmented tools and manual processes with purpose-built systems designed for how Anduril actually builds.

This is a zero-to-one role. You will build this product from the ground up , from vision through MVP to scaled adoption.

You must be able to zoom all the way in to improve how a single quality engineer dispositions a finding, and zoom all the way out to design the systems, data models, and integrations that connect quality execution to manufacturing, supply chain, and sustainment.

Every defect we catch earlier, every supplier interaction we accelerate, and every quality workflow we automate helps Anduril build and field critical technology faster.

Define and own the product strategy and roadmap for the Forge Portal, with a clear point of view on the what, why, and why now.

Build Forge Portal from the ground up , define the vision, scope the MVP, ship it, and iterate based on real user feedback and business outcomes.

Partner with quality engineering, supplier quality, reliability, manufacturing, and supply chain to define and deliver the software that connects their workflows into one coherent operating environment.

Define the integration strategy between quality systems and adjacent platforms , MES, ERP, PLM, sustainment , ensuring the right data flows to the right people at the right time.

Work with security engineering on authentication, authorization, and access control , ensuring external stakeholders can interact with Anduril systems safely and at the right level of access.

Leverage AI and modern tooling to accelerate product development , from prototyping and requirements generation to building intelligent workflows that surface insights, triage exceptions, and reduce manual decision-making.

Establish metrics and feedback loops that tie product work to measurable business outcomes, e.g., escape rates, supplier response times, cost of quality, inspection throughput.

Own prioritization, scope, tradeoff decisions, release planning, and execution quality across a cross-functional engineering team.

Drive adoption so business teams and external partners actually use what you build , and spend less time fighting software and more time ensuring hardware excellence.

Work in the open: document decisions, communicate roadmap and tradeoffs clearly, and create durable artifacts that align stakeholders and accelerate execution.

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Our team owns some of the most critical surface area in the product , Namespaces, Audit Logs, and Export. Namespaces are the core unit of isolation within Temporal Cloud, providing security boundaries, workflow management, unique identifiers, and dedicated gRPC endpoints. Audit Logs and Export give customers reliable access to the operational and compliance data they depend on.

In this role, you'll set technical direction and build systems that directly shape how customers provision, operate, and extract value from Temporal Cloud. You'll partner closely with engineers, product managers, and solutions architects to design resilient architectures, drive cross-functional execution, and raise the bar on reliability and scalability for our most critical features.

If you thrive on ambiguous, high-leverage problems and take pride in building durable systems that grow with the business, we'd love to meet you.

Key Responsibilities:

• Lead the Design of Business-Critical Systems: Own end-to-end technical design and delivery for the services that power namespace scalability, reliability, and the customer data products built on top of them.
• Bridge Instrumentation and Product Value: Partner with analytics and product engineers to ensure what gets collected actually powers meaningful customer insights; translate business questions into durable event schemas that outlast the feature that introduced them.
• Architect Data Pipelines for Scale and Correctness: Define and evolve our data pipeline architecture for high-throughput usage and operational data; make principled tradeoffs across data freshness, query cost, tenant isolation, and fault-tolerance.
• Drive Data Quality as a First-Class Concern: Establish schema contracts, pipeline-level anomaly detection, and alerting for silent failures , holding the line on data correctness before bad numbers reach customers.
• Raise the Engineering Bar: Set quality standards through code and design reviews; introduce patterns and tooling that improve maintainability, observability, and incident response across the team.
• Mentor and Multiply Impact: Grow the engineers around you, unblock execution, and foster a culture of ownership, learning, and pragmatic excellence.
• Improve Developer and Customer Experience: Identify opportunities to streamline customer flows via automation, better tooling, and targeted platform improvements that reduce friction and support load.

Requirements:

• Deep experience designing and delivering production-grade backend systems in cloud environments, with direct ownership over architecture and operational outcomes , including what happens when things go wrong.
• Strong proficiency in at least one systems or backend language (Go preferred), with the ability to navigate large codebases and drive technical decisions with confidence.
• Hands-on experience building and operating distributed systems: concurrency, reliability, scaling, consistency, and failure handling in production.
• Solid experience with data pipelines and storage systems (Postgres, Redshift, Kafka/Kinesis, S3-compatible storage, Clickhouse) and a track record of ensuring correctness and traceability at scale.
• Experience with production operations: observability, incident response, performance tuning, on-call readiness, and continuous delivery.
• Ability to work effectively across functions , translating ambiguous requirements from product managers and solutions architects into clear, executable technical plans.
• Strong written and verbal communication: you write crisp design docs, align stakeholders on tradeoffs, and can explain a complex system to someone who didn't build it.
• A builder's mindset: you enjoy simplifying complex workflows, improving tooling, and leaving systems better than you found them.

What You Can Expect:

• High-impact ownership over systems tied directly to revenue, customer lifecycle, and platform scalability
• Collaborative engineering culture focused on quality, pragmatism, and measurable outcomes
• Exceptional technical colleagues and a feedback-rich environment
• Lean processes and autonomy without unnecessary bureaucracy
• Strong emphasis on reliability and operational excellence,balanced with rapid iteration
• A team that values mentorship, thoughtful design, and steady improvements that compound over time

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As AI systems become more autonomous and more deeply integrated into real workflows, teams need practical infrastructure for observing behavior, measuring progress, catching regressions, and iterating with confidence.

Our roadmap is increasingly focused on agentic development and automated agent improvement: giving teams the infrastructure they need to compare versions, understand behavior, and make empirically grounded improvements over time.

Responsibilities:

• Design and implement Python-first APIs, SDK workflows, and plugin interfaces for building, measuring, and improving agents across multiple runtimes and product surfaces

• Build reusable systems for observing behavior, measuring progress, detecting regressions, and turning runtime evidence into product decisions

• Build systems for ingesting, normalizing, validating, and analyzing agent execution data and evaluation datasets

• Partner with research, product, platform, and infrastructure teams to integrate agentic capabilities broadly across NVIDIA agent runtimes and developer workflows

• Help turn emerging agent development and improvement techniques into reliable, reusable product capabilities

• Improve reliability, observability, debuggability, and performance across NeMoStack services, SDKs, plugins, jobs, and developer workflows

• Build strong test coverage across unit, integration, E2E, Docker, and Kubernetes workflows

• Drive “speed of light” engineering: fast iteration, high ownership, pragmatic decisions, and performance-minded implementation under production constraints

• Provide senior technical leadership through design reviews, code reviews, mentoring, and ownership of ambiguous cross-component problems

Requirements:

• BS, MS, or equivalent experience in Computer Science, Computer Engineering, or a related technical field

• 5+ years of professional software engineering experience building production systems

• Excellent Python engineering skills, including API design, typing, testing, debugging, performance analysis, and maintainable software design

• Experience designing SDKs, libraries, plugins, CLIs, or other developer-facing interfaces

• Experience with distributed systems, cloud-native services, containers, Kubernetes, or job orchestration

• Strong understanding of reliability, scalability, security, and performance tradeoffs in production infrastructure

• Experience with structured data modeling and validation systems such as Pydantic, typed schemas, event/trace models, or SDK-generated types

• Ability to work independently, define technical scope, break down ambiguous problems, and drive work across team boundaries

• Clear communication skills and a track record of collaborating with engineering, product, research, or customer-facing teams

Preferred Qualifications:

• Experience building, deploying, and iterating on production agentic AI systems where evaluation was used to measure and improve real product outcomes

• Experience designing evaluation workflows for heterogeneous agents, including tool-using agents, RAG agents, workflow agents, coding agents, or long-running autonomous systems

• Experience integrating evaluation capabilities across multiple products, runtimes, or internal platforms, especially through Python SDKs, plugins, or shared developer tooling

• Strong ability to connect technical evaluation work to business outcomes, product quality, user experience, reliability, or operational efficiency

• Experience with enterprise AI systems where measurement, regression testing, observability, governance, and continuous improvement are required for production deployment

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This role requires a rare blend of deep technical expertise across embedded systems, signal processing, and edge software, paired with the leadership skills to drive execution across multiple programs and heterogeneous hardware platforms. You will own the strategy, roadmap, and delivery for all software running on our radar systems,from FPGA-adjacent firmware integration and embedded Linux platforms to signal processing pipelines, fleet deployment, and operational tooling.

Responsibilities:

• Technical Leadership & Vision: Define and execute the software strategy and architectural roadmap for the radar product line, ensuring alignment with broader Anduril platform goals, mission needs, and customer requirements.

• Team Building & Management: Recruit, mentor, and grow a world-class software organization spanning embedded systems, signal processing, edge deployment, and DevSecOps. Build a culture of ownership, technical excellence, and rapid execution.

• Program Execution & Delivery: Own end-to-end software delivery across multiple radar programs, including planning, resourcing, milestone tracking, and risk management. Ensure on-time delivery of high-quality, field-deployable software.

• Cross-Functional Partnership: Collaborate with hardware, firmware, RF, systems engineering, product, and program management leaders to ensure seamless integration of software with radar hardware and broader Anduril systems (e.g., Lattice).

• Edge Software & DevSecOps: Drive the strategy for deploying, managing, and securing software running on embedded, heterogeneous processors within our radar systems,including fleet-wide updates for devices with intermittent or contested connectivity.

• Security & Compliance (DevSecOps): Champion security best practices across the software lifecycle, including STIG compliance, kernel hardening (ASLR, CFI, secure boot), software supply chain security, and robust controls on embedded hardware. Ensure programs meet customer and regulatory cybersecurity requirements.

• CI/CD & Developer Productivity: Set the direction for CI/CD infrastructure (e.g., CircleCI) and developer tooling that enables rapid, reliable build, test, and deployment of software to edge devices.

• Operating Systems & Platforms: Guide the strategy for embedded Linux distributions (Yocto, NixOS) optimized for performance, reliability, and security on ARM and GPU architectures.

• Heterogeneous Architecture Strategy: Lead software execution across our diverse hardware platform, including:

• Nvidia Jetson: Deployment, optimization, and management of software on Nvidia Jetson GPUs.

• FPGAs: Integration with HDL firmware for FPGAs (Altera Agilex, Xilinx Ultrascale) and C++ applications for soft processors, leveraging toolchains such as Quartus and Vivado.

• CPU Architectures: Software delivery and lifecycle management on ARM CPU platforms.

• Operational Excellence: Establish standards, processes, and metrics for code quality, reliability, observability, and field support. Lead troubleshooting and incident response for fielded systems.

Required Qualifications:

• Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field.

• 10+ years of software engineering experience, with 5+ years leading and managing software teams, including managing managers or large IC organizations.

• Track record of delivering complex, mission-critical software on embedded or edge systems, ideally in defense, aerospace, robotics, or comparable domains.

• Deep technical foundation in Linux internals, embedded systems, and system-level software architecture.

• Experience driving DevSecOps practices, including CI/CD (e.g., CircleCI), declarative configuration management (Nix/NixOS preferred), and containerization (Docker) in resource-constrained edge environments.

• Strong understanding of cybersecurity principles for embedded and edge devices, including core hardening techniques and secure software supply chain practices.

• Hands-on familiarity with hardware-in-the-loop (HITL) and system integration lab (SIL) workflows.

• Demonstrated ability to set technical vision, drive cross-functional execution, and influence at the executive level.

• Excellent communication, organizational, and problem-solving skills.

• Currently possesses and is able to maintain an active U.S. Secret security clearance.

Desired Skills:

• Prior experience leading software for radar, EW, RF sensing, or other signal processing-intensive systems.

• Familiarity with FPGA development workflows and tools such as Quartus and Vivado.

• Experience with bootloaders (U-Boot) and secure boot processes.

• Knowledge of network protocols and security on embedded systems.

• Experience deploying embedded systems in air-gapped or intermittently connected environments.

• Experience working with U.S. DoD customers, programs of record, and associated security and compliance frameworks.

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As an Engineering Manager, UI Platform, you will:

• Lead and grow the UI Platform team, setting technical direction for reliability, performance, and developer experience across all Claude.ai surfaces
• Own the foundational systems that product teams depend on: shared components, build and deploy pipelines, observability, and performance infrastructure
• Drive improvements to developer velocity,reducing friction, improving tooling, and enabling teams to ship features faster and more safely
• Establish and maintain high standards for latency, reliability, and quality across the platform
• Partner closely with product engineering, infrastructure, and leadership to align platform investments with product priorities
• Create clarity and direction in a fast-moving, ambiguous environment where priorities shift as Claude.ai evolves
• Build a culture of technical excellence, collaboration, and continuous improvement within your team

To be successful in this role, you'll need:

• 10+ years of software engineering experience, with significant time spent building platforms, developer tools, or infrastructure for consumer-facing products
• 5+ years of engineering leadership experience, including managing and growing teams in high-growth environments
• Deep expertise in modern web development stacks (React, Next.js, TypeScript)
• Passion for developer experience and a track record of building tools and systems that make other engineers more productive
• Strong opinions on reliability, observability, and performance,and know how to operationalize those opinions at scale
• Ability to balance technical depth with strategic thinking, staying hands-on while also setting long-term direction
• Thrive in ambiguity and create structure and clarity for your team when the path forward isn't obvious
• Effective communication skills across functions and ability to build alignment with product, design, and infrastructure partners

Strong candidates may also have experience with:

• Building and scaling platforms for multiple surfaces (web, mobile, desktop, extensions) with shared infrastructure
• Performance optimization and latency reduction in consumer-facing applications
• CI/CD, build systems, and deployment automation at scale
• Observability and monitoring systems (metrics, logging, tracing)
• Working on AI/ML products or in rapidly evolving product environments

The annual compensation range for this role is $405,000-$485,000 USD.

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At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff , payroll, health insurance, 401(k)s, and HR , so owners can focus on their craft and their customers.

With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.

All full-time employees receive competitive base pay, benefits, and equity (RSUs) , because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.

AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.

About the Role:

Gusto sits at the centre of many of the most important workflows for small businesses, which creates a meaningful opportunity to use rich product and customer data to build AI- and ML-powered systems that improve customer experiences, automate complex work, support better decision-making, and help small businesses thrive.

As Gusto becomes more AI-native, we are evolving how AI, ML, risk modeling, and platform capabilities come together across our products and internal systems. We are seeking a strategic Head of AI/MLE to lead this next chapter.

In this key leadership role, you will define how Gusto builds, deploys, evaluates, and scales AI/ML systems across the company. You will lead a broad organisation spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, while partnering closely with senior business leaders to shape where AI can create durable customer and business impact.

As the Head of AI/MLE at Gusto, you will be responsible for unifying classical ML and GenAI into a coherent technical strategy, maturing the platform for broader self-service adoption, and shaping how AI-native products and production systems are built at Gusto.

Your teams will help translate business problems into end-to-end AI/ML systems, from experimentation and prototyping through evaluation, deployment, monitoring, feedback loops, and operational governance.

Your leadership will be critical in setting the technical direction, operating model, and quality bar for AI at Gusto. You will help teams move quickly where speed and learning matter most, while ensuring production systems meet high standards for reliability, measurement, safety, and long-term maintainability.

This is a senior technical executive role for someone who can combine deep engineering credibility, strong business judgment, and executive-level influence to make AI a durable advantage for Gusto.

Here’s what you’ll do day-to-day:

• Lead, manage, and develop a broad AI/MLE organisation spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, fostering a culture of technical excellence, customer impact, collaboration, and continuous learning.

• Define and execute Gusto’s AI/ML systems strategy, unifying classical ML, GenAI, risk modeling, and platform capabilities into a coherent approach that supports Gusto’s broader business and product goals.

• Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business teams to identify where AI/ML can create meaningful customer value, business impact, and operational leverage.

• Shape how AI-native products and internal systems are built at Gusto, helping teams translate business problems into end-to-end AI/ML systems with clear standards for evaluation, monitoring, observability, reliability, safety, governance, and long-term maintainability.

• Lead the development and maturation of AI/ML platform capabilities, tooling, primitives, guardrails, and deployment patterns that make it easier for product and engineering teams to build, evaluate, deploy, and operate AI/ML systems with less friction, more autonomy, and the right quality bar.

• Drive disciplined technical and business judgment around AI/ML investments, including where to build, where to leverage existing capabilities, and where to avoid unnecessary complexity.

• Create room for fast experimentation and learning where appropriate, while ensuring high-impact production systems meet strong standards for quality, operational rigor, and business accountability.

• Set clear goals, KPIs, and operating rhythms to measure the performance, adoption, and business impact of AI/ML systems, and communicate progress and tradeoffs clearly to senior leadership.

• Stay close to the frontier of AI/ML advancement and help Gusto apply new technologies pragmatically, with strong judgment about what is durable, useful, and ready for production.

Here’s what we're looking for:

• We love meeting people with different technical and leadership backgrounds. For this role, we are looking for 10+ years of experience leading teams in applied machine learning, AI, engineering, or data science roles, with a track record of delivering impactful customer-facing software solutions.

• Deep technical expertise across AI/ML systems, including classical ML, GenAI/LLMs, statistical modeling, risk modeling, and production-scale deployment.

• Strong software engineering and systems background, with the ability to lead technical strategy across data, retrieval, evaluation, deployment, routing, monitoring, observability, feedback loops, and lifecycle management.

• Experience leading and scaling high-performing technical organisations, including Machine Learning Engineers, AI/ML Platform teams, Risk Data Scientists, and/or AI Scientists.

• Experience evolving ML teams toward a stronger software engineering and systems orientation, with clear ownership for building, operating, and improving production AI/ML systems.

• Strong platform orientation, with experience building tools, primitives, guardrails, and self-service capabilities that help product and engineering teams build AI/ML-powered products safely and effectively.

• Executive-level strategic judgment, with the ability to shape company-level AI/ML priorities, align senior leaders around tradeoffs, and make clear investment decisions based on customer value, business impact, technical feasibility, risk, data readiness, and operational complexity.

• Strong executive communication and influence, with the ability to explain complex AI/ML concepts and technical decisions in a way that clarifies strategy, tradeoffs, risk, investment needs, and organisational implications.

• Experience operating as a peer to senior cross-functional leaders across product, engineering, design, data, risk, legal, security, and business teams , bringing clarity, urgency, and practical judgment to ambiguous company-level opportunities.

• A clear thesis on how classical ML and GenAI should work together, how modern AI platform capabilities like retrieval, evaluation, agents, and observability should come together, and how AI/ML teams should evolve as the field becomes more software- and systems-oriented.

• Experience in fintech, risk modeling, regulated environments, or domains with high standards for reliability, trust, and compliance is a plus.

• Advanced degree in computer science, data science, machine learning, statistics, or a related field is a plus, but demonstrated systems leadership, production judgment, and executive-level impact matter most.

Our cash compensation amount for this role is targeted at $245,000-272,000 in Denver & most remote locations, and $288,000-321,000 for San Francisco & New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

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Your primary responsibilities will include:

• Providing day-to-day tactical direction, applying technical skills, maintaining operations, and resolving routine issues to ensure safe, efficient, productive, and environmentally sound manufacture of active ingredients or intermediates.

• Assisting plant management and operations personnel in evaluating and achieving plant performance goals related to safety, quality, waste minimization, capacity, reliability, and operability, making tactical decisions based on plant knowledge.

• Coordinating with corporate and site production planners on production timing and raw material availability.

• Development and tracking of key performance indicators.

• Participating in investigations of area performance issues related to safety, quality, environmental impact, capacity, reliability, and operability, leading to proposal and implementation of sound technical solutions.

• Serving as process expert or plant representative for design, construction, and implementation of investment projects, interfacing technical professionals from other disciplines.

• Maintaining and developing standards for area operating documentation.

• Collaborating on design and execute plant trials directed at enhancing area performance.

• Compiling, formatting, and submitting environmental data necessary for timely and accurate filing with local, state, and federal agencies.

• Engaging in continued development of self and area personnel through peer coaching and constructive feedback and support Site and Area Production System improvements.

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At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff , payroll, health insurance, 401(k)s, and HR , so owners can focus on their craft and their customers.

We're looking for a Head of ML/AI Engineering to lead our AI/ML efforts and help us build AI-native products and internal systems. As the Head of AI/MLE at Gusto, you will be responsible for unifying classical ML and GenAI into a coherent technical strategy, maturing the platform for broader self-service adoption, and shaping how AI-native products and production systems are built at Gusto.

Responsibilities

• Lead, manage, and develop a broad AI/MLE organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, fostering a culture of technical excellence, customer impact, collaboration, and continuous learning.

• Define and execute Gusto’s AI/ML systems strategy, unifying classical ML, GenAI, risk modeling, and platform capabilities into a coherent approach that supports Gusto’s broader business and product goals.

• Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business teams to identify where AI/ML can create meaningful customer value, business impact, and operational leverage.

• Shape how AI-native products and internal systems are built at Gusto, helping teams translate business problems into end-to-end AI/ML systems with clear standards for evaluation, monitoring, observability, reliability, safety, governance, and long-term maintainability.

• Lead the development and maturation of AI/ML platform capabilities, tooling, primitives, guardrails, and deployment patterns that make it easier for product and engineering teams to build, evaluate, deploy, and operate AI/ML systems with less friction, more autonomy, and the right quality bar.

• Drive disciplined technical and business judgment around AI/ML investments, including where to build, where to leverage existing capabilities, and where to avoid unnecessary complexity.

• Create room for fast experimentation and learning where appropriate, while ensuring high-impact production systems meet strong standards for quality, operational rigor, and business accountability.

• Set clear goals, KPIs, and operating rhythms to measure the performance, adoption, and business impact of AI/ML systems, and communicate progress and tradeoffs clearly to senior leadership.

• Stay close to the frontier of AI/ML advancement and help Gusto apply new technologies pragmatically, with strong judgment about what is durable, useful, and ready for production.

Requirements

• 10+ years of experience leading teams in applied machine learning, AI, engineering, or data science roles, with a track record of delivering impactful customer-facing software solutions.

• Deep technical expertise across AI/ML systems, including classical ML, GenAI/LLMs, statistical modeling, risk modeling, and production-scale deployment.

• Strong software engineering and systems background, with the ability to lead technical strategy across data, retrieval, evaluation, deployment, routing, monitoring, observability, feedback loops, and lifecycle management.

• Experience leading and scaling high-performing technical organizations, including Machine Learning Engineers, AI/ML Platform teams, Risk Data Scientists, and/or AI Scientists.

• Experience evolving ML teams toward a stronger software engineering and systems orientation, with clear ownership for building, operating, and improving production AI/ML systems.

• Strong platform orientation, with experience building tools, primitives, guardrails, and self-service capabilities that help product and engineering teams build AI/ML-powered products safely and effectively.

• Executive-level strategic judgment, with the ability to shape company-level AI/ML priorities, align senior leaders around tradeoffs, and make clear investment decisions based on customer value, business impact, technical feasibility, risk, data readiness, and operational complexity.

• Strong executive communication and influence, with the ability to explain complex AI/ML concepts and technical decisions in a way that clarifies strategy, tradeoffs, risk, investment needs, and organizational implications.

• Experience operating as a peer to senior cross-functional leaders across product, engineering, design, data, risk, legal, security, and business teams , bringing clarity, urgency, and practical judgment to ambiguous company-level opportunities.

• A clear thesis on how classical ML and GenAI should work together, how modern AI platform capabilities like retrieval, evaluation, agents, and observability should come together, and how AI/ML teams should evolve as the field becomes more software- and systems-oriented.

• Experience in fintech, risk modeling, regulated environments, or domains with high standards for reliability, trust, and compliance is a plus.

• Advanced degree in computer science, data science, machine learning, statistics, or a related field is a plus, but demonstrated systems leadership, production judgment, and executive-level impact matter most.

Our cash compensation amount for this role is targeted at $245,000-272,000 in Denver & most remote locations, and $288,000-321,000 for San Francisco & New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

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Responsibilities:

• Responsible for NVIDIA board team product management, including product yield management, drives and ensure product engineering implementation, maintenance of manufacturing methods, processes, and operations for new and existing products, product change control and build management, product failure debug and resolution over TOP issues.
• Review the project design and mfg. processes, identify potential risks, and develop and demonstrate solutions for GeForce, Quadro, and Tesla products.
• Evaluate and develop manufacturing solutions for new technologies and provide valuable suggestions.
• Drive manufacturing readiness and analyze extensive manufacturing data to drive product and mfg. processes improvement.

Requirements:

• Master in EE, CS, CE, System Engineering or equivalent experience.
• 8+ years of proven experience in Server/board HW design, Power, DFX, and manufacturing-related activities.
• In-depth understanding of one or more areas: Power, High-speed connector, Memory, HBM, SMT, Product Quality, Reliability, Advanced manufacturing, Automation and Agentic AI.
• Strong problem-solving and debugging expertise.
• Solid teamwork and proficiency in English.

We offer competitive salaries and a generous benefits package. As one of the most desirable employers in the technology world, we are proud to have individuals who consistently exhibit high levels of performance and expertise working for us.

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Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organisations to safely embrace this new era.

We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.

The Passwordless Team

Passkeys, powered by FIDO2 WebAuthn, are revolutionising how people authenticate,bringing us into a passwordless future that’s more secure and seamless than ever before.

At Okta, we’re on a mission to help enterprises and consumers embrace this transformation. We’re building cutting-edge phishing-resistant authenticators and passkey solutions that make security stronger while making life easier for users. Our products empower businesses to leave passwords behind and defend against modern threats with innovative, user-friendly technology.

The Staff Software Engineer opportunity

We are looking for a Staff Software Engineer to join the Passwordless team. As a Staff Engineer, you will be a technical leader, making large, independent contributions and shaping the future of how millions of users access technology securely.

This is a role for an engineer who thrives on solving ambiguous problems and taking ownership of end-to-end solutions. You will not only execute on complex technical challenges but also influence the team's direction, mentor other engineers, and proactively identify opportunities to improve our products and processes.

If you are passionate about building highly reliable, scalable systems and want to define the future of passwordless authentication, we’d love for you to join us.

What you’ll be doing

• Partner closely with product, design, and other cross-functional stakeholders to own problem spaces and ensure we are building the right solutions for our customers.

• Independently own and deliver major features from conception and design through to deployment and monitoring, tackling ambiguous problems along the way.

• Act as a technical leader within the team, driving design discussions, influencing the roadmap, and making strategic trade-offs that improve team velocity and product health.

• Mentor other engineers through code reviews, knowledge sharing, and by setting an example of engineering craft and operational excellence.

• Champion a data-driven culture by leveraging telemetry and observability tools to troubleshoot complex production incidents and guide technical decisions

What you’ll bring to the role

• 8+ years of professional software development experience, with a demonstrated history of technical leadership and mentorship.

• Deep expertise in Java, including a strong understanding of object-oriented design, multithreading, and building high-performance services.

• Proven experience designing, building, and operating complex, large-scale distributed systems in a cloud environment (e.g., AWS, GCP, Azure).

• A strong grasp of architectural patterns for scalability, reliability, and security, with the ability to lead technical design discussions and make informed trade-offs.

• Excellent problem-solving skills, with the ability to independently debug and remediate issues in complex, unfamiliar codebases and systems.

• A proactive and ownership-driven mindset, with the ability to manage your own priorities and spot areas for future work.

• Strong communication and collaboration skills, with a history of working effectively with cross-functional teams to deliver impactful products.

• Deep knowledge of phishing-resistant authentication methods, including Passkeys, FIDO2, or WebAuthn.

• Experience in identity and access management (IAM), including protocols like OAuth, OIDC, and SAML.

And extra credit if you have experience in any of the following!

• Experience with modern frontend frameworks (e.g., React, Angular, Vue.js)

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This role will partner with engineering leaders in product engineering, infrastructure, client platforms, data science, and senior technical leadership to make performance a measurable, durable, and consistently protected part of how Reddit builds and ships software.

Performance is foundational to user trust, engagement, and long-term growth. This role will help Reddit build the path for sustained performance improvement: broad enough to work across platforms and organizations, focused enough to address current issues, and measurable enough to prove whether we are improving the lived experience of users.

Just as importantly, this role will help bring teams along for that change, building trust across functions and helping Reddit evolve how we make performance part of our engineering culture.

Responsibilities

You will own and drive programs across Reddit's Performance portfolio, spanning mobile and web performance, client reliability, critical user journeys, observability, experimentation guardrails, release quality, and long-term performance operating mechanisms.

You will partner deeply with engineering teams as a technical peer, not from the stratosphere, but in the details, bringing programs to completion, balancing risk across systems and organizations, and ensuring execution stays aligned to Reddit's long-term performance strategy.

In this role, you will:

• Partner with Client Platforms, product engineering, Infrastructure, data science, and senior technical leaders to translate performance strategy into executable programs across mobile, web, and shared platform systems.

• Define and drive the operating model for sustained performance improvement, including how teams prioritize work, how success is measured, how regressions are prevented, and how risks are escalated or resolved.

• Drive the organizational change required for performance to become a durable part of Reddit’s engineering culture, moving teams from reactive efforts to shared standards and repeatable mechanisms.

• Develop communication and enablement mechanisms that bring engineers and leaders along for the journey, including leadership forums, working groups, documentation, decision reviews, and clear narratives that connect performance work to user and business outcomes.

• Build measurement systems that distinguish real user experience improvement from shallow metric movement.

• Drive performance governance and adoption in a way that preserves team ownership while ensuring user experience, reliability, release quality, experimentation discipline, and business guardrails are not afterthoughts.

• Use technical judgment to challenge assumptions, identify systemic risks, and guide tradeoffs across client architecture, observability, experimentation, release health, and production reliability.

Requirements

• 14+ years of experience across technical program management, engineering, infrastructure, client platforms, mobile or web platforms, performance engineering, reliability, observability, or related technical domains.

• Led multi-year, multi-organization technical programs with executive visibility and ambiguous ownership.

• Deep fluency in performance and reliability systems: client platforms, app health, observability, experimentation, release quality, user journey measurement, infrastructure, or production readiness.

• Understand performance beyond individual metrics. Can reason about user experience, platform tradeoffs, technical debt, regression prevention, and the organizational mechanisms required to make improvements last.

• Highly data-driven, but skeptical of shallow metrics. Know how to build measurement systems that connect technical improvements to user, business, and engineering outcomes.

• Exceptional cross-functional operator and communicator. Bring people together, create clarity through the right leadership channels, build trust across functions, and translate complex technical and organizational change into clear narratives.

• Led meaningful organizational change, not just program execution. Know how to shift behavior across teams by listening to concerns, creating shared language, and making the desired path easier than the status quo.

• Get things done. Not satisfied with status reporting, process theater, or activity metrics. Land outcomes and help teams understand the difference between motion and impact.

Preferred Qualifications

• Experience leading performance, reliability, client platform, mobile platform, web platform, infrastructure modernization, observability, or experimentation programs at large scale.

• Experience with mobile or web performance, including app startup, rendering, media delivery, networking, caching, crashes, hangs, release quality, or user journey performance.

• Experience building durable operating mechanisms after incident, war room, or Code Yellow style mobilizations.

• Experience with experimentation systems, holdouts, guardrail metrics, and tradeoff decisions where user experience, engagement, and business metrics need to be evaluated together.

• Experience building adoption strategies for internal platforms, observability systems, performance tooling, or engineering practices.

• Experience working in fast-moving environments where technical strategy, organizational design, and execution mechanisms are evolving at the same time.

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Your work directly impacts engineering velocity across the entire company: defining the abstractions for how code moves from idea to production, establishing the metrics that surface friction before it compounds, and making the trade-offs that keep a rapidly scaling engineering organization shipping with confidence.

You'll drive the evolution of our developer platform through a fundamental shift in how software gets built,as AI agents move from autocomplete to autonomous collaborators, the definition of 'developer' is changing, and our tooling, governance, and workflows must change with it. You'll be defining what developer productivity means when a meaningful share of code is written, tested, and reviewed by Claude itself.

Responsibilities: Deeply understand the needs of internal customers across Research, Inference, Infrastructure, and Product,from researchers iterating on training code who need fast, reproducible builds to inference engineers managing compute-intensive toolchains with strict compatibility constraints. Define and iterate on the developer experience model: the workflows, tooling primitives, and feedback loops that govern how engineers and AI agents collaborate on code,including how we measure productivity when the unit of work is no longer a human typing. Partner with engineering leads to design build, CI, and test infrastructure that scales non-linearly with engineering headcount,ensuring that as Claude takes on more of the inner loop, the outer loop (review, validation, deployment) doesn't become the new bottleneck. Drive product strategy and roadmap for developer acceleration, including AI-assisted code review, agent-driven test generation, automated dependency management, and the governance frameworks that let teams safely delegate work to autonomous systems. Own the trade-off framework between velocity, reliability, security, and cost,making transparent prioritization decisions about where to invest in human workflows versus agent workflows, and communicating them clearly to senior leadership. Establish and champion the productivity metrics that matter in an AI-native engineering org,moving beyond commits and cycle time to measures that capture human-agent collaboration effectiveness, toil eliminated, and time-to-confident-ship. Build conviction about where developer tooling is headed on a 2–3 year horizon, and translate that into a roadmap that keeps Anthropic ahead of,not reacting to,the exponential curve of AI-assisted development.

You may be a good fit if you have: 7+ years of product management experience, with deep exposure to developer tooling, build systems, CI/CD, or platform infrastructure Experience taking technical platform products from infancy to scale,you've built something from the ground up and grown it to serve demanding internal or external engineering customers Track record of building platform products that balance the needs of multiple engineering personas,you're comfortable making prioritization trade-offs between velocity, reliability, and security, and communicating them clearly Ability to internalize complex technical systems (build systems, monorepos, CI pipelines, accelerator toolchains) and translate that understanding into a comprehensive product vision Fluent across functions,you're equally credible discussing build graph optimization with engineers, developer velocity economics with leadership, and AI-agent governance with security teams A strong thesis on how AI will reshape software development,you've thought deeply about what changes when agents write, review, and ship meaningful portions of a codebase, and you're energized by defining the tooling for that world rather than waiting for it to arrive Scrappy and resourceful,you do what it takes to get things done in a fast-moving environment

Strong candidates may have: Built or scaled developer productivity, build systems, or CI/CD platforms for large engineering organizations (e.g., Bazel, Buck, large-scale monorepos, or custom build infrastructure) Experience defining and operationalizing engineering productivity metrics (DORA, SPACE, or custom frameworks),and a point of view on how these metrics evolve when AI agents are in the loop Familiarity with accelerator toolchain ecosystems (CUDA/GPU, TPU, or AWS Neuron/Trainium) and the unique developer experience challenges of compute-intensive ML workloads Shipped AI-native developer tooling,code assistants, agent-based automation, or AI-integrated IDEs,and understand the governance, trust, and adoption challenges that come with it Scaled through hypergrowth in engineering-intensive environments (AI/ML, large-scale cloud infrastructure, or developer tools companies) Experience with internal platform adoption,you know that the best internal tool is the one engineers actually use, and you've driven adoption through product quality rather than mandate

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Your work directly impacts engineering velocity across the entire company: defining the abstractions for how code moves from idea to production, establishing the metrics that surface friction before it compounds, and making the trade-offs that keep a rapidly scaling engineering organization shipping with confidence.

You'll drive the evolution of our developer platform through a fundamental shift in how software gets built,as AI agents move from autocomplete to autonomous collaborators, the definition of 'developer' is changing, and our tooling, governance, and workflows must change with it.

You will define and own the strategy and roadmap across build systems, CI/CD pipelines, developer environments, accelerator toolchain management (GPU, TPU, Trainium), and the AI-native acceleration layer that makes Anthropic the most productive place in the world to build frontier AI.

Responsibilities:

Deeply understand the needs of internal customers across Research, Inference, Infrastructure, and Product,from researchers iterating on training code who need fast, reproducible builds to inference engineers managing compute-intensive toolchains with strict compatibility constraints.

Define and iterate on the developer experience model: the workflows, tooling primitives, and feedback loops that govern how engineers and AI agents collaborate on code,including how we measure productivity when the unit of work is no longer a human typing.

Partner with engineering leads to design build, CI, and test infrastructure that scales non-linearly with engineering headcount,ensuring that as Claude takes on more of the inner loop, the outer loop (review, validation, deployment) doesn't become the new bottleneck.

Drive product strategy and roadmap for developer acceleration, including AI-assisted code review, agent-driven test generation, automated dependency management, and the governance frameworks that let teams safely delegate work to autonomous systems.

Own the trade-off framework between velocity, reliability, security, and cost,making transparent prioritization decisions about where to invest in human workflows versus agent workflows, and communicating them clearly to senior leadership.

Establish and champion the productivity metrics that matter in an AI-native engineering org,moving beyond commits and cycle time to measures that capture human-agent collaboration effectiveness, toil eliminated, and time-to-confident-ship.

Build conviction about where developer tooling is headed on a 2–3 year horizon, and translate that into a roadmap that keeps Anthropic ahead of,not reacting to,the exponential curve of AI-assisted development.

You may be a good fit if you have:

7+ years of product management experience, with deep exposure to developer tooling, build systems, CI/CD, or platform infrastructure

Experience taking technical platform products from infancy to scale,you've built something from the ground up and grown it to serve demanding internal or external engineering customers

Track record of building platform products that balance the needs of multiple engineering personas,you're comfortable making prioritization trade-offs between velocity, reliability, and security, and communicating them clearly

Ability to internalize complex technical systems (build systems, monorepos, CI pipelines, accelerator toolchains) and translate that understanding into a comprehensive product vision

Fluent across functions,you're equally credible discussing build graph optimization with engineers, developer velocity economics with leadership, and AI-agent governance with security teams

A strong thesis on how AI will reshape software development,you've thought deeply about what changes when agents write, review, and ship meaningful portions of a codebase, and you're energized by defining the tooling for that world rather than waiting for it to arrive

Scrappy and resourceful,you do what it takes to get things done in a fast-moving environment

Strong candidates may have:

Built or scaled developer productivity, build systems, or CI/CD platforms for large engineering organizations (e.g., Bazel, Buck, large-scale monorepos, or custom build infrastructure)

Experience defining and operationalizing engineering productivity metrics (DORA, SPACE, or custom frameworks),and a point of view on how these metrics evolve when AI agents are in the loop

Familiarity with accelerator toolchain ecosystems (CUDA/GPU, TPU, or AWS Neuron/Trainium) and the unique developer experience challenges of compute-intensive ML workloads

Shipped AI-native developer tooling,code assistants, agent-based automation, or AI-integrated IDEs,and understand the governance, trust, and adoption challenges that come with it

Scaled through hypergrowth in engineering-intensive environments (AI/ML, large-scale cloud infrastructure, or developer tools companies)

Experience with internal platform adoption,you know that the best internal tool is the one engineers actually use, and you've driven adoption through product quality rather than mandate

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Responsibilities:

• Design, implement, and maintain Infrastructure as Code (IaC) solutions using Terraform and/or CloudFormation across multi-account AWS environments.
• Collaborate with developers, architects, and DevOps teams to build scalable, secure, and observable cloud infrastructure.
• Lead and participate in architecture design sessions, focusing on system reliability, scalability, security, and performance.
• Implement and manage robust monitoring, alerting, and observability solutions (e.g., CloudWatch, Prometheus, ELK, Datadog).
• Set and monitor Key Performance Indicators (KPIs) for system uptime, latency, throughput, and overall reliability.
• Drive incident response processes, including coordination, triaging, resolution, documentation, and post-incident reviews (PIRs).
• Supervise and mentor junior SREs and infrastructure engineers, fostering knowledge-sharing and team growth.
• Collaborate across development, operations, and security teams to ensure secure and compliant deployments.
• Automate manual tasks and workflows through scripting and tooling (Python, Node.js, Bash, Ruby, JSON/YAML).
• Troubleshoot complex infrastructure issues across Linux, Windows, Docker, and cloud-native environments.
• Provide IaC and CI/CD best practices to ensure repeatability, scalability, and compliance across all environments.
• Provide on-call support, participate in incident rotations, and lead technical investigations during outages or degradations.
• Strong understanding and experience for Disaster Recovery (DR).
• Provide support and solution handling to incident and tickets assigned.

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This role will drive the platform’s expansion to deliver critical systems and services that support analytics, machine learning, and business intelligence. You’ll help define strategy, align stakeholders, and ensure execution at scale. This role sits at the intersection of hardware engineering, data, and infrastructure, driving programs that power scalable, reliable, and efficient data platforms.

Responsibilities:

• Own and drive end-to-end delivery of large-scale data infrastructure programs (ex: data lake, data platforms, pipelines, etc)
• Partner with Hardware engineering and data engineering teams to define roadmaps, scope, and technical requirements
• Translate ambiguous business needs into structured plans with clear milestones, risks, and dependencies
• Manage cross-team coordination, program governance, status tracking, reporting, and stakeholder communication
• Identify and mitigate risks, resolve blockers, and drive timely decisions across all initiatives
• Improve operational efficiency by introducing scalable processes, tools, and best practices
• Ensure reliability, scalability, performance, data quality, observability, and governance standards are embedded in all initiatives
• Drive post-launch evaluations, retrospectives, and continuous improvement

Benefits:

• Highly competitive salaries
• Comprehensive benefits package
• Eligible for equity

Requirements:

• B.S. (or equivalent experience) in Computer Science or a related technical field
• 12+ years of technical program management experience, including ownership in data infrastructure, distributed systems, or on-prem platforms at large scale (multi-TB/day)
• Familiarity with data ecosystems (ex: ETL/ELT pipelines, data lakes/warehouses, etc) and technologies like Spark, Kafka, Airflow, Snowflake, or similar ecosystems
• Strong understanding of system design concepts such as scalability, reliability, and performance
• Demonstrated experience partnering with highly technical engineering teams and driving products from concept through delivery
• Analytical approach, including defining success measures, tracking adoption, and finding opportunities to improve workflow efficiency
• Excellent cross-functional leadership and communication skills, with the ability to influence without authority across multiple teams
• Experience working in environments with geo-distributed teams and complex collaboration requirements
• Track record of driving adoption of internal platforms and influencing engineering culture

Preferred Qualifications:

• Experience supporting hardware or silicon development environments, including pre-silicon development workflows
• Background with working on modern data platforms (e.g., real-time streaming, large-scale batch processing) in on-prem or hybrid environments with infrastructure-as-code practices
• Experience in data governance, privacy, or compliance initiatives
• Experience supporting or scaling AI/ML platforms and infrastructure, with an understanding of the tooling, workflows, and operational requirements that ML teams need
• Familiarity with FinOps principles and cloud cost optimization strategies, including resource tagging, rightsizing, and cost allocation frameworks to drive infrastructure efficiency at scale

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The ideal candidate combines deep technical expertise with strong experience in research engagement and proven track record to translate advanced technologies into impactful, research-driven outcomes.

Responsibilities: Act as a trusted technical advisor for research labs, identifying and accelerating high-impact workloads by integrating NVIDIA's frameworks, libraries, and core software stack into research projects. Map and continuously assess the research ecosystem to identify institutional growth opportunities and inform long-term technology strategies Stay current on research papers across affiliated domains to anticipate emerging trends and provide technical direction on future collaboration areas. Collaborate cross-functionally with Research Account Managers, Solution Architects, and Business Development teams to drive researcher enablement Forge closer ties with lab personnel, understanding organizational dynamics and the full scope of research being conducted. Attend domain scientific conferences and facilitate NVIDIA participation alongside other subject matter experts. Represent the needs of researchers internally by translating academic feedback into actionable insights that shape product development, education programs, and platform roadmaps.

Requirements: PhD in Computer Science, AI, Machine Learning, Computational Science, Applied Mathematics, or a related technical field; or equivalent experience demonstrating comparable research depth. 3+ years of experience Deep expertise in agentic AI systems, including LLM agents, tool use, planning, reasoning, memory, retrieval, code execution, workflow automation, and multi-agent systems. Strong understanding of frontier research challenges in agents, including long-horizon task completion, evaluation, reliability, safety, observability, alignment, and failure recovery. Hands-on experience building or evaluating agent systems using modern AI frameworks, model APIs, orchestration tools, retrieval systems, sandboxed execution, and research-grade deployment patterns. Ability to engage leading AI labs on autonomous systems, tool learning, multimodal agents, embodied agents, scientific agents, and collaborative human-AI workflows. Research credibility through publications, prototypes, open-source work, academic collaborations, or technical leadership in agentic AI, foundation models, autonomous systems, or applied AI research.

Preferred Qualifications: Experience with NVIDIA technologies and platforms such as CUDA, CUDA-X libraries, NVIDIA AI Enterprise, NIM, NeMo/Nemotron, NeMo Agent Toolkit, Omniverse, Isaac, RAPIDS, TensorRT, Triton Inference Server, or DGX/accelerated computing systems. Established relationships with leading academic labs, research institutes, national labs, or major open source research communities. Track record translating frontier research into demos, reference architectures, workshops, technical content, or developer enablement programs. Experience presenting at academic conferences, research workshops, technical summits, or university facing events. Ability to identify emerging research trends and convert them into strategic opportunities for collaboration, platform adoption, and ecosystem growth.

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Developer Success is the team customers rely on when they need fast, confident support for complex developer and infrastructure issues. The team helps users deploy, operate, debug, and scale Temporal across cloud-native environments, distributed systems, SDKs, observability stacks, and mission-critical workloads.

The next phase requires a leader who can preserve technical excellence and customer empathy while building the operational rigor of a world-class support function.

Responsibilities

• Build and lead Global Developer Success: Hire, coach, develop, retain, and performance-manage high-performing support engineers, managers, and specialists across tiers, regions, and customer segments.
• Create the support operating system: Define SLAs/OLAs, ticket and Slack triage, queue ownership, severity definitions, tiering, escalation paths, incident communications, RCA standards, quality review, staffing, and operating cadence.
• Raise the quality bar: Move beyond CSAT alone by measuring customer effort, touches to resolution, reopen rate, handoffs, recurrence, communication quality, resolution confidence, and customer-visible ownership.
• Harden Slack and customer response: Turn premium Slack response into a defined service model with acknowledgement standards, urgent paths, conversion-to-ticket rules, coverage model, manager review, and customer-facing expectations.
• Protect Engineering focus through better support: Reduce avoidable escalations by building runbooks, diagnostics, knowledge systems, reproducibility standards, technical triage, and high-quality escalation packets.
• Lead escalation and incident discipline: Establish crisp roles for technical lead, escalation manager, communications owner, Support, Engineering, SA, Platform Architecture, and Sales. Ensure incidents and critical escalations are communicated with clarity and followed by useful RCAs.
• Build technical depth and enablement: Create career paths, training, mentorship, certification, quality review, and hands-on learning that help junior engineers become senior/staff support engineers and help senior engineers scale their expertise.
• Partner across the company: Work closely with Product, Engineering, Sales, Solutions Architecture, Platform Architecture, Customer Success, Docs, DevRel, and Services to ensure support signal turns into product, documentation, enablement, and customer lifecycle improvements.
• Use tooling and AI pragmatically: Improve routing, deflection, summaries, analytics, knowledge surfacing, diagnostics, and customer communication while measuring real operating outcomes rather than tool adoption alone.
• Represent Temporal with customers: Engage directly in strategic escalations, high-stakes incidents, enterprise support conversations, QBR/EBR inputs, and executive readouts when support experience affects customer trust.

Requirements

• 12+ years of experience in technical support, customer engineering, SRE, solutions engineering, infrastructure engineering, or similar customer-facing technical roles, with 5+ years leading support or customer-facing engineering teams.
• Proven experience building and scaling technical support operations for a deeply technical B2B product, ideally in developer tools, cloud infrastructure, data platforms, observability, AI infrastructure, open source, or distributed systems within a GTM organization.
• Experience managing managers and building the systems that make teams run: metrics, operating cadence, queue management, quality review, staffing models, tiering, escalation paths, and performance management.
• Strong technical credibility with developers, support engineers, SREs, platform engineers, and product engineers. You can reason about APIs/SDKs, cloud-native infrastructure, Kubernetes, observability, distributed systems, reliability, incident response, and debugging workflows.
• Track record reducing avoidable Engineering escalations while improving the quality of product and engineering signal when escalations are needed.
• Deep understanding of support quality metrics: response time, resolution time, customer effort, touches, reopens, recurrence, backlog, SLA/OLA attainment, deflection, CSAT, sentiment, and case quality.
• Strong customer communication skills in high-pressure situations. You can write crisp updates, lead customer-facing RCA conversations, set expectations, and coach others to communicate clearly.
• Clear philosophy for hiring, onboarding, leveling, progression, coaching, retention, and performance management for technical support engineers and support managers.
• High ownership and builder mindset. You create clarity in ambiguous systems, ship v1 processes quickly, and iterate with data and customer evidence.

Nice to Have

• Experience with Temporal, Cadence, workflow engines, orchestration systems, event-driven architecture, distributed applications, or production reliability tooling.
• Experience in consumption-based, open-source, developer-led, or mission-critical enterprise support motions.
• Energized by working within a GTM organization.
• Experience with support tooling and modern AI support systems such as Pyl

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$230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

AI Systems Engineer - Codex Core Agents

About The Team

The Codex Core Agents team builds the agent harness that turns model capability into real-world action. We own the systems around the model: prompting and interpreting model outputs, executing actions safely in real environments, and feeding production experience back into better models and better agent behavior.

This team sits close to research and works across the stack: harness, model interaction, inference, sandboxed execution, orchestration, evals, production reliability, and the performance envelope around tokens, latency, cost, capacity, and quality. The harness is open source and increasingly part of how models are trained and evaluated, making this one of the highest-leverage layers in Codex.

About The Role

We’re looking for engineers to build the AI systems that make Codex agents dependable in production. The ideal candidate is an agent-systems builder: hands-on across low-level systems and ML workflows, able to debug Codex behavior end to end across the harness, model behavior, inference/runtime stack, GPU fleet, and product surface.

You’ll work with research, infrastructure, and product to design agent harness capabilities, run experiments and ablations across the model + system prompt + harness stack, build frameworks for assessing production agent performance, and turn messy failures into durable improvements.

What You’ll Do

• Design and build the core agent harness and execution loop that lets Codex agents interpret model outputs, use tools, execute code, and complete long-horizon tasks safely.

• Build sandboxing, isolation, orchestration, state, and workflow infrastructure for agents operating in real development environments.

• Develop evaluation, experimentation, and debugging systems that distinguish harness issues, model behavior, inference/runtime issues, and product failures.

• Run ablations across prompts, model-facing interfaces, context construction, tool-use strategies, and harness behavior to improve solve rate, reliability, latency, and cost.

• Improve observability, profiling, and diagnostics across the agent stack, from backend systems to inference, GPUs, and fleet capacity.

• Work closely with research to make the harness trainable, measurable, and useful for improving frontier agentic models.

• Build shared primitives that make Codex faster, safer, more reliable, and easier for other teams and open-source users to build on.

You Might Be A Good Fit If You

• Have built or operated production systems in distributed systems, infrastructure, developer tooling, sandboxing, virtualization, cloud platforms, or ML systems.

• Enjoy working across layers: Rust systems code, Python configuration layers, APIs, agent orchestration, evals, logs/traces, inference behavior, runtime constraints, and user outcomes.

• Have hands-on experience with LLM applications, coding agents, evals, model deployment, inference, compiler/runtime performance, or developer platforms.

• Care deeply about reliability, safety, performance, debuggability, and clean abstractions.

• Can debug from evidence and move quickly from ambiguous production failures to practical, durable fixes.

• Want to work close to research while still shipping changes to production

• Still write meaningful code, show strong ownership, and can lead scoped or multi-team AI systems work.

Bonus Points

• Deep Rust, systems, sandboxing, isolation, or low-level platform experience.

• Experience with coding agents, agent harnesses, tool-using LLM systems, model evals, or post-training feedback loops.

• Background in compilers, kernels, runtimes, inference optimization, GPU systems, benchmarking, profiling, or performance engineering.

• Experience building production infrastructure used by many engineers or users under demanding reliability and security constraints.

• Open-source infrastructure or developer-platform work with strong taste for APIs and usability.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see [OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement](https://cdn.openai.com/policies/eeo-policy-statement.pdf).

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected

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Your responsibilities will include designing and implementing test strategies, validation approaches, and release readiness criteria for AI-enabled software, automation, and agentic workflows. You will partner closely with software and AI engineers to identify failure modes early across code, prompts, models, integrations, infrastructure, and user workflows.

You will build confidence in fast-moving solutions through automated testing, observability, instrumentation, environment design, and operational safeguards. You will help define practical standards for reliability, resiliency, debugging, and production operations in an AI-first development model.

You will contribute directly in code, infrastructure, and tooling where needed to improve delivery confidence, developer feedback loops, and production stability. You will support launch readiness, production issue response, root cause analysis, and continuous improvement for front-facing systems delivered by the pod.

You will evangelize strong engineering discipline in areas such as quality assurance, release engineering, infrastructure hygiene, and incident prevention without sacrificing speed.

This role requires 5+ years of experience in software engineering, site reliability engineering, quality engineering, infrastructure engineering, or a closely related role in a fast-paced environment.

The ideal candidate will have demonstrated experience building or operating reliable production software systems, including ownership of testing, observability, deployment confidence, and operational readiness.

They will have strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or platform infrastructure.

Experience working with frontier AI tooling, AI coding assistants, LLM-enabled applications, or agentic systems, including awareness of the unique quality and reliability challenges these systems introduce, is highly desirable.

The successful candidate will have excellent written and verbal communication skills, with the ability to influence senior engineers and cross-functional stakeholders on quality and reliability trade-offs.

A degree in Computer Science, Information Systems, Engineering, or related technical field, or equivalent practical experience, is required.

U.S. Person status is required as this position needs to access export-controlled data.

Eligibility to obtain and maintain a U.S. Secret security clearance is also preferred.

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Your responsibilities will include designing and implementing test strategies, validation approaches, and release readiness criteria for AI-enabled software, automation, and agentic workflows. You will partner closely with software and AI engineers to identify failure modes early across code, prompts, models, integrations, infrastructure, and user workflows.

Your key objectives will be to build confidence in fast-moving solutions through automated testing, observability, instrumentation, environment design, and operational safeguards. You will help define practical standards for reliability, resiliency, debugging, and production operations in an AI-first development model.

In addition, you will contribute directly in code, infrastructure, and tooling where needed to improve delivery confidence, developer feedback loops, and production stability. You will support launch readiness, production issue response, root cause analysis, and continuous improvement for front-facing systems delivered by the pod.

You will evangelize strong engineering discipline in areas such as quality assurance, release engineering, infrastructure hygiene, and incident prevention without sacrificing speed.

In this role, you will have the opportunity to work with frontier AI tooling, AI coding assistants, LLM-enabled applications, or agentic systems, including awareness of the unique quality and reliability challenges these systems introduce.

To succeed in this position, you will need to have strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or platform infrastructure. You will also need to have experience working with AI-first or AI-accelerated software development teams, including designing quality controls around non-deterministic system behavior.

We are looking for a highly skilled and experienced individual who can bring a strong understanding of quality engineering principles and practices to this role. If you are passionate about delivering high-quality software and have a strong background in AI development, we encourage you to apply.

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As a Principal, Data Science and Analytics in the team, your major responsibilities include:

Leadership: Mentor data scientists and align work with MAI ecosystem goals, driving technical excellence, innovation, and cross-team collaboration. Data Strategy & Execution: Develop ecosystem data strategies for marketplace and system performance, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results. Advanced Analytics & Measurement: Apply machine learning, statistical modeling, data mining, and experimentation to large datasets; define and deliver metrics that accurately measure user and business value across products and marketplace components. Experimental Design & Implementation: Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders. Collaboration: Partner closely with product, program management, engineering, and business teams to integrate data science solutions into shared platforms and marketplace operations. Performance Optimization: Identify cross-team opportunities for product and process improvement; implement data-driven solutions to improve efficiency, reliability, and user experience. Influence & Decision-Making: Engage stakeholders with clear, compelling, and actionable insights; make independent decisions for the team and handle complex tradeoffs to drive product and service improvements. Technical & Operational Leadership: Develop and standardize processes for data acquisition, quality, and operationalizing ML models; provide expert review of analysis and modeling; lead adoption of new tools and technologies to improve availability, reliability, efficiency, and performance. Standards & Trusted Advisory: Establish and uphold standards, policies, and best practices for high-quality, efficient, and extensible code; influence business, customer, and solution strategy with a strong customer focus; act as a trusted advisor across the ecosystem.

Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor’s Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

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This is not a traditional product design role. You'll go beyond interface design to define how AI systems behave, operate, and deliver value in the real world,similar to emerging "AI Model Designer" roles, but more deeply embedded in product.

What You'll Do

1. Define AI capabilities (the "what and why")

• Identify and pattern match high-value opportunities for AI across the product
• Define what AI should do,and just as importantly, what it should not do, making hard or unpopular calls when needed
• Set clear boundaries for autonomy vs human control

1. Design AI behavior, orchestration, and system logic

• Architect AI-driven workflows and agentic systems that operate across real-world scenarios and over time, including multi-step and agent-to-agent interactions
• Establish decision frameworks: when systems should act, ask, escalate, or defer
• Design for uncertainty, failures, and edge cases as core system behavior
• Develop and apply design principles and heuristics to shape how AI systems behave and make decisions
• Ensure systems remain coherent and predictable, even as complexity scales

1. Shape human + AI workflows and end-to-end experience

• Design experiences for users who are increasingly managing AI systems and agents
• Design how AI integrates into real user workflows, not just isolated interactions
• Define the role of the human in the loop: where users guide, review, or override
• Ensure users can understand, trust, and recover from system failures
• Partner with product designers to continuously shape and adapt the product experience as underlying systems evolve

1. Define quality, evaluation, and reliability

• Define what "good" looks like (e.g. trust, accuracy, effort reduction, and whether outputs are structured and usable in context)
• Design evaluation scenarios and feedback loops
• Analyze outputs to identify failure modes and system weaknesses
• Design for reliability at the system level, preventing agentic breakdowns

How You'll Work

• Shape direction from the earliest stages of zero-to-one problems
• Influence across teams without formal authority
• Operate in a highly autonomous, fast-moving environment with evolving constraints
• Lead ambiguous, in-flight work,making calls on when to adapt the product vs. push on system improvements as things evolve
• Act as a bridge between AI research, technical capabilities, and user needs
• Collaborate deeply with ML scientists, PMs, and product designers
• Prototype and run focused experiments that isolate variables and generate clear insights
• Stay close to system behavior by regularly reviewing outputs and raising the bar on quality

What We're Looking For

• Strong product judgment,you consistently make high-quality calls on what to build and why
• Ability to influence without authority
• Experience working on zero-to-one products and comfort operating through ambiguity and rapid change
• Deep understanding of LLMs and their limitations, along with a grounding in traditional ML approaches and when to use them instead
• Experience defining design principles, heuristics, or evaluation criteria for AI systems
• High standards for quality, with a track record of identifying where AI systems break down in real-world use
• A scientific mindset,you can design experiments that isolate variables and produce meaningful insights
• Systems thinker: you're happier designing behaviors and flows than polishing interfaces

What This Role Is Not

• You will not be training models or building ML infrastructure
• You will not be responsible for backend architecture or performance optimization
• You will only occasionally design UI,this is not the focus of the role

You will influence all of these,but your primary responsibility is defining what should be built, how it should behave, and how it performs in the real world.

Why This Role Is Compelling

• You'll join a mature AI discipline operating at the frontier. Intercom already has an established AI Design team and strong cross-functional understanding of this role, so you can focus on building rather than justifying your value.
• You won't be "adding AI to a product." The quality of our AI is the quality of our product. This is a technical, hands-on role where you have the satisfaction of working with tight feedback loops.
• You'll shape product direction, not just execution. You'll operate upstream in a high-autonomy environment with real ownership over outcomes.

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At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff , payroll, health insurance, 401(k)s, and HR , so owners can focus on their craft and their customers.

With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.

All full-time employees receive competitive base pay, benefits, and equity (RSUs) , because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.

AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.

About the Role:

You'll design, build, and scale the AI platform that empowers Gusto's internal teams to build intelligent agents and deliver customer value. This includes developing core platform capabilities , agent orchestration, RAG infrastructure, eval tooling, model serving, prompt management, observability, and safety guardrails , that enable product teams to rapidly build, test, and ship AI-powered experiences. You'll own the full lifecycle from problem framing to production deployment, ensuring the platform is reliable, performant, and easy to adopt.

As a Staff MLE, you'll also shape technical standards, drive architectural decisions, and mentor engineers across the organization.

About the Team:

You'll join the CoreX AI Platform team, responsible for building the foundational AI infrastructure that Gusto's internal app teams rely on to create and ship intelligent agents. We're a small, high-impact group of ML engineers and platform builders working closely with Product Engineers, PMs, and Designers across the company. We move fast, prototype boldly, and focus on making it easy for any team to build and deploy AI-powered experiences.

What you'll do day-to-day:

Design and build scalable platform services , agent orchestration, RAG pipelines, eval frameworks, model serving , that internal teams use to ship AI-powered products.

Lead technical strategy and architecture for the AI platform, including model lifecycle, observability, safety guardrails, and evaluation infrastructure.

Collaborate cross-functionally with app teams, PMs, and Designers to understand their AI needs and deliver platform capabilities that unblock them.

Build, harden, and operate shared infrastructure that powers intelligent agents across Gusto (e.g., retrieval, routing, prompt management, content selection).

Stay current with AI/ML research; rapidly prototype and productionize new techniques that strengthen the platform.

Establish robust practices for validation, deployment, monitoring, and ongoing performance management of platform services and the agents built on them.

Communicate technical strategy, tradeoffs, and impact clearly to executives and non-technical partners.

Mentor engineers across teams, raising the bar on AI/ML best practices and fostering a culture of pragmatic innovation.

Here’s what we’re looking for

12+ years building and deploying end-to-end AI/ML systems, with experience designing platform-level infrastructure that other engineering teams build on.

Deep expertise in one or more areas: LLMs, NLP, retrieval/RAG, agent orchestration, deep learning, or reinforcement learning.

Hands-on experience building LLM-based applications and agentic workflows , including prompt engineering, retrieval design, evaluation, and production deployment.

Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face) and cloud platforms (GCP, AWS, or Azure).

Strong Python skills and sound software engineering fundamentals , testing, code review, CI/CD, reliability, and API design.

Proven track record shipping impactful AI/ML projects to production, ideally in a platform or infrastructure context.

Demonstrated ability to lead cross-functionally, influence technical direction, and communicate clearly with both engineers and non-technical stakeholders.

Ph.D. or Master's in CS, ML, Statistics, Mathematics, or related field is a plus.

Compensation

Our cash compensation amount for this role is targeted at $245,000-272,000 in Denver & most remote locations, and $288,000-321,000 for San Francisco & New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

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At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff , payroll, health insurance, 401(k)s, and HR , so owners can focus on their craft and their customers.

With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.

All full-time employees receive competitive base pay, benefits, and equity (RSUs) , because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.

AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.

About the Role

You'll design, build, and scale the AI platform that empowers Gusto's internal teams to build intelligent agents and deliver customer value. This includes developing core platform capabilities , agent orchestration, RAG infrastructure, eval tooling, model serving, prompt management, observability, and safety guardrails , that enable product teams to rapidly build, test, and ship AI-powered experiences. You'll own the full lifecycle from problem framing to production deployment, ensuring the platform is reliable, performant, and easy to adopt.

As a Staff MLE, you'll also shape technical standards, drive architectural decisions, and mentor engineers across the organization.

About the Team

You'll join the CoreX AI Platform team, responsible for building the foundational AI infrastructure that Gusto's internal app teams rely on to create and ship intelligent agents. We're a small, high-impact group of ML engineers and platform builders working closely with Product Engineers, PMs, and Designers across the company. We move fast, prototype boldly, and focus on making it easy for any team to build and deploy AI-powered experiences.

What you'll do day-to-day:

• Design and build scalable platform services , agent orchestration, RAG pipelines, eval frameworks, model serving , that internal teams use to ship AI-powered products.
• Lead technical strategy and architecture for the AI platform, including model lifecycle, observability, safety guardrails, and evaluation infrastructure.
• Collaborate cross-functionally with app teams, PMs, and Designers to understand their AI needs and deliver platform capabilities that unblock them.
• Build, harden, and operate shared infrastructure that powers intelligent agents across Gusto (e.g., retrieval, routing, prompt management, content selection).
• Stay current with AI/ML research; rapidly prototype and productionize new techniques that strengthen the platform.
• Establish robust practices for validation, deployment, monitoring, and ongoing performance management of platform services and the agents built on them.
• Communicate technical strategy, tradeoffs, and impact clearly to executives and non-technical partners.
• Mentor engineers across teams, raising the bar on AI/ML best practices and fostering a culture of pragmatic innovation.

Here’s what we’re looking for

• 12+ years building and deploying end-to-end AI/ML systems, with experience designing platform-level infrastructure that other engineering teams build on.
• Deep expertise in one or more areas: LLMs, NLP, retrieval/RAG, agent orchestration, deep learning, or reinforcement learning.
• Hands-on experience building LLM-based applications and agentic workflows , including prompt engineering, retrieval design, evaluation, and production deployment.
• Proficiency with modern ML frameworks (PyTorch, TensorFlow, Hugging Face) and cloud platforms (GCP, AWS, or Azure).
• Strong Python skills and sound software engineering fundamentals , testing, code review, CI/CD, reliability, and API design.
• Proven track record shipping impactful AI/ML projects to production, ideally in a platform or infrastructure context.
• Demonstrated ability to lead cross-functionally, influence technical direction, and communicate clearly with both engineers and non-technical stakeholders.
• Ph.D. or Master's in CS, ML, Statistics, Mathematics, or related field is a plus.

Compensation

Our cash compensation amount for this role is targeted at $245,000-272,000 in Denver & most remote locations, and $288,000-321,000 for San Francisco & New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.

About the role

Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users , demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand.

Node Infra owns the full lifecycle of accelerator capacity at Anthropic. We ingest and provision compute from all major CSPs and our own datacenters, stand up and scale clusters from thousands to hundreds of thousands of hosts, and build the health, diagnostics and repair automation that keep every GPU, TPU and Trainium node in the fleet usable and ready to power Anthropic’s frontier AI research.

Key responsibilities

• Own the technical strategy and roadmap for node lifecycle management - ingestion, bring-up, health checking, and automated repair

• Drive cross-team initiatives to build and scale AI clusters across multiple clouds and accelerator families

• Design and operate the systems that detect, isolate, and remediate unhealthy hardware automatically, driving up fleet MTBI and minimizing stranded capacity

• Define infrastructure architecture, ensuring the hardest problems get solved - whether by you directly or by working through others

• Work closely with cloud providers and internal research/inference/product teams to shape long-term compute, data, and infrastructure strategy

• Establish and evolve operational excellence practices (incident response, postmortem culture, on-call)

• Support the growth of engineers around you through technical mentorship and coaching

Minimum qualifications

• Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure)

• Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform.

• Hands-on experience with machine learning accelerators (GPUs, TPUs, or Trainium)

• Track record of leading complex, multi-quarter technical initiatives that span multiple teams or systems

• Ability to build alignment across senior stakeholders and communicate effectively at all levels

Preferred qualifications

• 8+ years of software engineering experience, including time as a technical lead setting direction for a team

• Experience managing large scale compute infrastructure at hyperscale (10K+ nodes), including capacity management and efficiency

• Depth in one or more of: Kubernetes internals (scheduler, autoscaler, kubelet, Karpenter), cluster orchestration systems (Mesos, Borg-like), or node provisioning pipelines

• Low-level systems experience: kernel, virtualization, device drivers, firmware, or hardware health/diagnostics daemons

• Familiarity with high-performance networking (EFA, RDMA, InfiniBand) for distributed ML workloads.

• Demonstrated ownership of production reliability for high-throughput, latency-sensitive systems

• Contributions to relevant open-source projects (Kubernetes, Linux kernel, container runtimes, etc.)

• Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems

Annual compensation range for this role is £325,000-£485,000 GBP.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.

About the role

Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users , demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand.

Cluster Infra owns the full lifecycle of compute clusters at Anthropic. We build agent-driven automation for cluster provisioning and lifecycle management across all major cloud providers and our own datacenters. Our systems stand up clusters that are interconnected with high bandwidth, secure-by-default, and able to automatically drain and recover in response to failure. As a Staff engineer on this team, you'll set the technical direction for how Anthropic brings compute online - at a moment when the scale of that compute is growing faster than at almost any company in the world.

Key responsibilities

• Own the technical strategy and roadmap for agent-driven cluster lifecycle management - provisioning, updates and decommissioning

• Partner across teams to ensure new compute capacity is ingested on time

• Align with partner teams on physical build-out and leverage cloud solutions to deliver high-bandwidth inter-cluster connectivity

• Collaborate with security owners to ensure clusters are provisioned secure-by-default

• Define and drive strategy on cluster scalability, homogeneity and fault tolerance

• Work closely with cloud providers and internal research, inference and product teams to shape long-term compute, data, and infrastructure strategy

• Establish and evolve operational-excellence practices: incident response, postmortem culture and on-call health

• Support the growth of engineers around you through technical mentorship and coaching

Minimum qualifications

• Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure)

• Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform.

• Track record of leading complex, multi-quarter technical initiatives spanning multiple teams or systems

• Ability to build alignment across senior stakeholders and communicate effectively at all levels

Preferred qualifications

• 8+ years of software engineering experience, including time as a technical lead setting direction for a team

• Experience operating large-scale compute infrastructure at hyperscale (100+ clusters, 10K+ nodes)

• Depth in one or more of: Kubernetes internals, cluster provisioning and management systems, cluster orchestration systems (Mesos, Borg-like)

• Experience with cloud networking: VPC design and peering, Shared VPC/Transit Gateway, Cloud Interconnect/Direct Connect, Cloud NAT, cross-cloud private connectivity, BGP and route control, edge load balancing and DDoS mitigation (Cloud Armor / AWS Shield)

• Experience with cluster and host networking: CNI (Cilium), eBPF, NetworkPolicy, multi-NIC, sFlow, service mesh (Istio/Envoy/Linkerd, mTLS)

• Experience with cluster security: pod security standards and admission control, RBAC and least-privilege IAM, node and container hardening, supply-chain/image provenance

• Deep experience with infrastructure-as-code (Terraform, Atlantis), workflow orchestration (Temporal, Argo Workflows)

• Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems

The annual compensation range for this role is listed below.

Annual Salary: £325,000-£485,000 GBP

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Your tasks:

• Provide customer service and advice in the service area
• Receive vehicles, including conducting a dialogue acceptance and explaining the work to be done on the vehicle to the customer
• Actively sell service offerings, parts, and accessories
• Implement premium processes according to Porsche Service Standards
• Ensure a seamless and professional service experience for customers
• Hand over repaired vehicles and explain the work done on the vehicle, as well as the invoice
• Conduct customer retention and acquisition measures
• Coordinate the service business

Requirements:

• Completed vocational training in a commercial or technical field
• Experience as a Service Advisor
• Ideally, a completed training as a certified Service Advisor
• Expertise in vehicle diagnosis and repair work
• Proficient use of service-specific tools such as service/workshop management
• Familiarity with MS Office and ideally also with the DMS system Cross
• Strong customer orientation, high team skills, and flexibility
• Sense of responsibility, assertiveness, reliability, and initiative

Exclusive insights behind the scenes:

• Secure job in a critical industry
• Above-average, performance-based remuneration
• Responsible and varied tasks
• Family-owned tradition company with a culture of appreciation
• Unlimited contract
• Up to 30 days' holiday
• Attractive benefits such as pensionable benefits, occupational pension scheme, fruit basket, cycle leasing, and exclusive employee benefits

Application guidelines:

We look forward to receiving your application, either by email with attachments in PDF format or by post. In both cases, please send us complete documents with application letter, CV, photo, and last school certificates.

Contact: Porsche Centre Garmisch-Partenkirchen, c/o Frau Katharina Keilhauer, An der Zugspitze 6, 82491 Grainau, Tel. +49 8821 9319-90, E-Mail: katharina.keilhauer@porsche-garmisch.de

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Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users , demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand.

Node Infra owns the full lifecycle of accelerator capacity at Anthropic. We ingest and provision compute from all major CSPs and our own datacenters, stand up and scale clusters from thousands to hundreds of thousands of hosts, and build the health, diagnostics and repair automation that keep every GPU, TPU and Trainium node in the fleet usable and ready to power Anthropic’s frontier AI research.

Key Responsibilities

• Own the technical strategy and roadmap for node lifecycle management - ingestion, bring-up, health checking, and automated repair

• Drive cross-team initiatives to build and scale AI clusters across multiple clouds and accelerator families

• Design and operate the systems that detect, isolate, and remediate unhealthy hardware automatically, driving up fleet MTBI and minimizing stranded capacity

• Define infrastructure architecture, ensuring the hardest problems get solved - whether by you directly or by working through others

• Work closely with cloud providers and internal research/inference/product teams to shape long-term compute, data, and infrastructure strategy

• Establish and evolve operational excellence practices (incident response, postmortem culture, on-call)

• Support the growth of engineers around you through technical mentorship and coaching

Minimum Qualifications

• Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure)

• Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform.

• Hands-on experience with machine learning accelerators (GPUs, TPUs, or Trainium)

• Track record of leading complex, multi-quarter technical initiatives that span multiple teams or systems

• Ability to build alignment across senior stakeholders and communicate effectively at all levels

Preferred Qualifications

• 8+ years of software engineering experience, including time as a technical lead setting direction for a team

• Experience managing large scale compute infrastructure at hyperscale (10K+ nodes), including capacity management and efficiency

• Depth in one or more of: Kubernetes internals (scheduler, autoscaler, kubelet, Karpenter), cluster orchestration systems (Mesos, Borg-like), or node provisioning pipelines

• Low-level systems experience: kernel, virtualization, device drivers, firmware, or hardware health/diagnostics daemons

• Familiarity with high-performance networking (EFA, RDMA, InfiniBand) for distributed ML workloads.

• Demonstrated ownership of production reliability for high-throughput, latency-sensitive systems

• Contributions to relevant open-source projects (Kubernetes, Linux kernel, container runtimes, etc.)

• Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems

Annual Compensation Range

The annual compensation range for this role is $320,000-$405,000 USD.

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

• Visa sponsorship: We do sponsor visas! However, we aren’t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How to Apply

If you’re interested in this role, please submit your application through our website. We look forward to hearing from you!

Contact Us

If you have any questions or would like to learn more about this role, please don’t hesitate to reach out to us. We’re always happy to chat!

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The common thread is strong engineering judgment and excitement about making enormous compute systems faster, more reliable, and easier to use.

Depending on your background and interests, you might work close to hardware, close to users, on CaaS and agent infrastructure, or on the control planes and data planes in between.

We do not expect every candidate to have worked at every layer. Some engineers will go deep on systems performance, kernel or runtime behavior, large-scale networking protocols, RDMA, NCCL, GPU hardware behavior, benchmarking, scheduling, or hardware reliability; others will make the platform more usable through APIs, tools, workflows, and developer experience.

This is a general opening for Compute Infrastructure. We will consider candidates for teams across Compute Infrastructure and match you based on your strengths, the problems that motivate you, and where the infrastructure needs are highest.

In this role, you will:

• Build and deeply optimize reliable system software for large-scale compute systems that run some of the world's most demanding AI workloads

• Design and operate infrastructure across accelerators, CPUs, NICs, switches, networking protocols, storage, data centers, cluster orchestration, scheduling, and fleet health

• Profile, benchmark, and optimize training workloads across compute, memory, storage, networking, NCCL and collective communication, and cluster scheduling bottlenecks

• Create hardware-aware automation that makes provisioning, firmware and driver upgrades, incident response, and day-to-day operations faster and less error-prone

• Build CaaS, agent infrastructure, profiling, observability, benchmarking, and platform tools that help researchers, product engineers, and operators launch, debug, and optimize workloads with less friction

• Turn operational lessons into better systems, stronger abstractions, and clearer ownership boundaries across teams

• Collaborate across research, engineering, security, networking, hardware, and data center teams to make compute capacity more capable and easier to use

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In this role, you will work across performance benchmarking, tokenomics, model porting, infrastructure integration, systems tooling, and operational monitoring. You will help connect partner and first-party compute environments into OpenAI's infrastructure stack, ensuring GPU capacity can be onboarded, measured, monitored, and optimized against real workload outcomes.

Key Responsibilities

• Develop systems and tooling to measure, monitor, and improve token throughput across first-party and partner-owned compute environments.
• Support performance benchmarking, tokenomics analysis, and model porting across heterogeneous infrastructure environments.
• Build tooling to integrate external or partner infrastructure into OpenAI's internal compute, observability, and workload management systems.
• Develop and monitor operational metrics including billing, usage, SLAs, utilization, reliability, and throughput.
• Identify bottlenecks across hardware, networking, software, and workload enablement that prevent capacity from becoming productive tokens.
• Partner with compute, infrastructure, networking, finance, and operations teams to translate raw capacity into usable workload-serving capacity.
• Build dashboards, automation, and reporting systems that provide clear visibility into TaaS capacity, performance, and business outcomes.

Qualifications

• Strong software engineering background with experience building systems, tooling, automation, or infrastructure platforms.
• Experience working across compute infrastructure, distributed systems, performance engineering, or production operations.
• Ability to reason about token throughput, utilization, benchmarking, infrastructure efficiency, and workload performance.
• Comfortable integrating external systems or partner environments into internal infrastructure stacks.
• Strong analytical and debugging skills across hardware, networking, software, and operational domains.

Preferred Skills

• Experience with GPU clusters, AI infrastructure, performance benchmarking, or workload optimization.
• Familiarity with model porting, inference/training workloads, token economics, or compute efficiency analysis.
• Experience building monitoring systems for billing, usage, SLAs, utilization, or infrastructure reliability.
• Background in systems engineering, infrastructure software, observability, distributed systems, or platform engineering.

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The Porsche Centre Bad Homburg is a successful dealership for one of the world's leading sports car brands. Our goal is to continue this success and provide our customers with the best possible service. To strengthen our team, we are offering a position as a Kfz-Mechatroniker (m/w/d).

Responsibilities

• Perform all maintenance services according to manufacturer's specifications and maintenance plan
• Diagnose and document inspection work
• Participate in training sessions (online or in-person)
• Handle visual inspections, installation work, justifications, and fuel changes
• Perform defined, possibly extensive assembly and repair work
• Install and commission accessories and components
• Perform functional tests for all complex repair work

Requirements

• Completed technical education, preferably as a Kfz-Mechatroniker/Kfz-Elektriker or a technical education with a comparable qualification, with work experience or a master's degree
• Communication skills and teamwork skills
• Experience in digital-assisted repair and maintenance
• Knowledge of the use of modern diagnostic methods
• Good knowledge of German in writing and speaking
• Technical knowledge in the sports car sector
• Strong interest in complex technical relationships and achieving solution-oriented results
• Reliability, sense of responsibility, and willingness to learn
• Independent and meticulous working style
• Master's degree
• High level of engagement and willingness to be part of a highly motivated team
• Class B driving license

We offer

• Responsible tasks
• Top-qualified and motivated colleagues
• Modern workplaces in a professional working environment
• Continuous training and targeted promotion
• Various career opportunities according to your talents
• Employee discounts
• 30 days of holiday

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In this role, you'll:

Define and own the strategy, roadmap, and execution for Twilio's SMS and RCS APIs, driving innovation while simplifying the developer experience.

Lead the evolution of our messaging platform, including launching new cross-channel APIs and evolving our legacy APIs at global scale.

Work backwards from developers and enterprises to design intuitive, consistent APIs that power billions of customer interactions

Gather market intelligence, analyze usage patterns, and synthesize insights from prototypes to innovate

Partner closely with engineering, design, GTM, and partner teams to scope, build, and launch capabilities

Drive experimentation through betas and A/B tests, iterating quickly to achieve product–market fit for emerging conversational experiences.

Use data and metrics to identify growth opportunities, performance gaps, and areas to improve reliability.

Mentor and influence teams across the org, championing a developer-first culture in a distributed, remote-first environment.

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio.

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You have a deep systems background and value technical excellence, and consistently raise the bar for your team's technical execution. You know how to balance providing value to your users while building defensive systems that are operationally sound and easy to maintain.

Core Responsibilities

• Partner with our extended leadership team to set and define a technical strategy for your team aligned with the wider team strategy.
• Build and champion a long-term tech roadmap to reduce operational burden, ensure scalability, reduce risk, and guide your team towards step-changes whenever possible.
• Be technically involved and engage in substantive discussion when reviewing technical roadmaps and project implementation with the team.
• Work closely with teammates and stakeholders to enable sustainable and timely delivery of technical solutions to address business needs.
• Facilitate partnerships between engineering teams and operators to build innovative products that help Palantir scale.
• Act as a multiplier for other engineers on the team. Define where the technical bar should be, and help engineers achieve it. Lead engineers and accelerate their growth by providing thoughtful feedback, technical mentorship, and effectively manage performance.
• Foster a non-hierarchical exchange of ideas; valuing the idea rather than the individual who communicates it.

What We Value

• Experience designing, building and operating high-scale observability or infrastructure systems.
• Clear and concise verbal and written communications.
• Experience leading complex, cross-team engineering efforts, including analysis of requirements and scoping, and knowing what it takes to deliver high-quality software.
• Demonstrated ability work in a fast-paced product development team, with peers and teammates across multiple locations and timezones.
• Willingness and enthusiasm to do what it takes to get things done, leading by example and getting involved with the team's day-to-day work.
• A focus on impact and an ability to articulate the value your work provides.
• Active US Security clearance, or eligibility and willingness to obtain a US Security clearance.

What We Require

• 5+ years of professional software development experience.
• 2+ years of experience contributing to the system design or architecture (architecture, design patterns, reliability and scaling) of new and existing systems.
• 1+ years of experience as a mentor, tech lead Or leading an engineering team.
• Strong coding skills in Go, Java, or equivalent.
• Experience designing, building, and operating high-scale observability or infrastructure systems.
• Bachelor's degree in Computer Science or equivalent.

Additional Information

• Salary: The estimated salary range for this position is estimated to be $135,000 - $200,000/year.
• Benefits: Palantir offers a comprehensive benefits package, including medical, dental, and vision insurance, as well as 401(k) matching and paid time off.

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Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.

As an iOS Engineer, you will design and build core features for Robinhood’s crypto trading experience on iOS. You will contribute to architecture decisions, improve app performance and reliability, and collaborate with engineers, designers, and product managers to deliver features that directly support customer trading activity and portfolio management.

This role is based in our New York, NY office with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

Work with a fast-growing team to revolutionize finance at speed and scale Build and enhance iOS features using Swift and UIKit and/or SwiftUI to support core customer experiences Contribute to mobile architecture and code structure to ensure scalability, reliability, and maintainability Optimize app performance and responsiveness using iOS profiling and debugging tools Write clean, maintainable, and well-tested code and standard iOS testing practices

Challenging, high-impact work to grow your career Performance driven compensation with multipliers for outsized impact, bonus programs, and equity ownership Top tier benefits to fuel your work, including supplemental health insurance, ancillary insurance, and mental health support programs Lifestyle wallet - a highly flexible employer-paid benefits spending account expenses beyond traditional benefits such as wellness, childcare, learning, and more. Time off to recharge including company holidays, paid time off, sick time, paid volunteer time off, parental leave, and more! Exceptional office experience with catered meals, events, and comfortable workspaces. Monthly commuter stipend to help offset in-office commuting costs

Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones. For other locations not listed, compensation can be discussed with your recruiter during the interview process.

Base Pay Range: Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC) $157,000-$185,000 USD Zone 2 (Denver, CO; Westlake, TX; Chicago, IL) $139,000-$163,000 USD Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL) $122,000-$144,000 USD

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The Director, Core Runtime & Platform Engineering leads the engineering organization responsible for the runtime, edge, connectivity, cloud infrastructure, developer platform, and reliability layers that enable CoreOS and FMIL to operate securely and at scale across plants, vehicle programs, and ATP product platforms.

This leader builds the platform backbone that connects industrial devices, controls, real-time signals, and manufacturing applications into a unified execution environment, while also delivering APIs, SDKs, and reusable platform services that allow teams to build once and scale consistently across Ford.

Reporting to the Director of Software, Controls & Diagnostics (CoreOS, FMIL & CoreDS), this role is central to delivering the runtime and signal layer that powers ATP's 'Build Once, Scale Everywhere' strategy and enables closed-loop learning across manufacturing systems.

Responsibilities:

1. Lead the Core Runtime Platform

• Architect and deliver the runtime platform that powers CoreOS and FMIL across Ford manufacturing environments

• Build a secure, scalable execution layer supporting orchestration, device integration, event processing, and platform interoperability

• Enable FMIL as the real-time signal, orchestration, and decisioning layer connecting factory systems and applications

• Establish platform standards for runtime services, deployment models, lifecycle management, and system interfaces

• Drive the evolution of Core from point solutions into a globally managed industrial software platform

1. Build the Core Developer Platform (APIs & SDKs)

• Define and deliver platform APIs, SDKs, and shared services enabling ATP products and engineering teams to build on Core

• Create reusable primitives for telemetry, eventing, orchestration, device abstraction, identity, and policy control

• Establish developer standards, tooling, and integration patterns to eliminate one-off solutions across plants and products

• Enable Forge, Sight, Print, Volt, and future platforms to extend Core through standardized interfaces and services

• Drive platform adoption by improving developer productivity, consistency, and reuse

1. Industrial Edge & Distributed Systems

• Lead development of edge computing platforms for real-time manufacturing environments

• Architect distributed systems supporting high-throughput, low-latency workloads across thousands of factory devices

• Build resilient edge runtimes supporting local processing, failover, and intermittent connectivity

• Establish orchestration patterns across cells, lines, plants, and global manufacturing networks

1. Device Connectivity & Industrial Middleware

• Own the middleware connecting PLCs, robotics, vision systems, and industrial equipment to Core platforms

• Standardize interfaces for telemetry, command/control, and interoperability across manufacturing systems

• Define integration patterns across protocols such as EtherCAT, Profinet, Ethernet/IP, CAN, OPC UA, MQTT, TCP/IP, and gRPC

• Build reusable adapters, APIs, and SDKs that simplify onboarding of factory devices and applications

• Ensure consistent publishing of factory signals into FMIL and CoreDS

1. Cloud & Hybrid Platform Infrastructure

• Architect and operate hybrid edge-cloud infrastructure connecting plant environments with centralized cloud platforms

• Build and manage cloud-based control planes for orchestration, fleet management, telemetry aggregation, and lifecycle control

• Design secure edge-to-cloud patterns enabling global visibility, coordination, and system-level optimization

• Develop cloud-native services supporting CoreOS, FMIL, and CoreDS at enterprise scale

• Partner with data and enterprise teams to enable scalable storage, compute, and processing capabilities

1. Real-Time Data, Telemetry & CoreDS Integration

• Build real-time ingestion and streaming systems connecting factory signals to CoreDS

• Ensure seamless integration between edge-generated data and cloud data platforms

• Enable high-fidelity data pipelines supporting AI/ML, diagnostics, analytics, and traceability

• Support closed-loop systems where factory signals → insights → actions continuously improve performance

• Contribute to manufacturing data models, access patterns, and system-wide data consistency

1. Observability, Diagnostics & Runtime Integrity

• Build platform-wide observability across edge and cloud environments using tools such as Prometheus, Grafana, and OpenTelemetry

• Enable real-time monitoring, alerting, and diagnostics across all deployments

• Ensure runtime and telemetry signals are high-quality, structured, and actionable

• Support diagnostics and AI/ML systems through reliable signal and data availability

• Improve transparency and operational trust across engineering, launch, and plant teams

1. Reliability, Deployment & Global Scale

• Define deployment, rollout, upgrade, and rollback strategies across edge, on-prem, and cloud environments

• Establish SRE and release engineering practices for mission-critical manufacturing systems

• Define SLAs for uptime, latency, reliability, and deployment stability

• Lead global rollout of Core platforms aligned to ATP lifecycle models (TRL/MRL/IRL) and launch playbooks

• Partner with Launch, Field, Compass, and product teams to enable repeatable, scalable deployments

1. Engineering Leadership & System Execution

• Build and lead a high-performing engineering organization across runtime, edge, infrastructure, connectivity, and platform domains

• Drive engineering excellence in architecture, SDLC, testing, release management, and operational rigor

• Develop senior technical talent and create clear career pathways tied to platform and business outcomes

• Align execution with ATP operating model, lifecycle governance, and platform priorities

• Translate strategy into roadmaps, staffing plans, and delivery execution

1. Cross-Functional Platform Leadership

• Partner with Controls, Diagnostics, Data, and ATP vertical teams to deliver integrated manufacturing platforms

• Align with plant operations, global manufacturing engineering, and vehicle programs

• Contribute to enterprise standards for software-defined manufacturing and industrial platform architecture

• Support system priorities including runtime adoption, AIR integration, diagnostics readiness, and platform reuse

• Serve as a key leader in shaping Ford's software-defined manufacturing platform strategy

What success looks like...

• A standardized Core runtime platform deployed across plants, programs, and ATP products

• A scalable edge-to-cloud operating model enabling global orchestration and visibility

• Broad adoption of APIs, SDKs, and platform services across engineering teams

• Reliable, high-quality factory data powering CoreDS, analytics, and AI/ML systems

• Closed-loop learning systems improving manufacturing performance through real-time signals

• Increased platform reuse across sites, reducing duplication and accelerating

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This role requires an in-depth understanding of part construction, the ability to identify potential quality or reliability risks, and the expertise to define and implement screening tests to expose and address these risks effectively.

Key responsibilities include:

• Dive into part construction to identify quality and reliability risks, ensuring components meet mission-critical standards.
• Collaborate with design engineers to guide component selection during the design process, ensuring reliability and compliance with Astranis standards.
• Plan, execute, and oversee component level screening and qualification testing.
• Review bills of materials for new designs to ensure designs are using approved flight parts.
• Manage component supplier part change notifications and drive appropriate responses to supplier PCNs.
• Execute tear-downs on critical components to assess the part quality and reliability.
• Interface with supply chain, design, and manufacturing teams to address component-related sourcing challenges.
• Manage the Astranis approved parts list in PLM to ensure accurate information provided to the engineering and manufacturing teams.

Requirements include:

• Bachelor's degree in electrical engineering, computer engineering, or a related field.
• Experience in electrical design, including components, circuits, PCBs, or electronic assemblies.
• 5+ years experience in part quality, reliability, failure analysis, and/or DPA.
• Familiarity with JEDEC, AEC-Q and MIL component standards.

Bonus requirements include:

• Experience in supply chain, purchasing, or manufacturing processes.
• Familiarity with manufacturing processes and testing methodologies for electronic components.
• Proficiency in non-destructive and destructive lab analysis techniques.
• Experience in reliability statistics and data analysis.
• Strong analytical skills and ability to thrive in a collaborative team environment.
• Excellent verbal and written communication skills.

What we offer:

All our positions offer a compensation package that includes equity and robust benefits. Base pay is just one component of Astranis's total rewards package. Your compensation also includes a significant equity package via incentive stock options, high-quality company-subsidized healthcare, disability and life insurance, 401(k) retirement planning, flexible PTO, and free on-site catered meals.

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The Porsche Centre Frankfurt is one of the most successful sports car dealerships in the world, representing Porsche. Our goal is to continue this success and provide our customers with the best possible service. To strengthen our team, we are offering a position as a

Motor Vehicle Technician (m/f/d)

• Perform all maintenance services according to manufacturer specifications and maintenance plan
• Diagnose and document inspection work
• Participate in training sessions (online or in-person)
• Support visual inspections, installation work, just-in-time deliveries, and fuel changes
• Perform defined, possibly extensive assembly and repair work
• Install and commission accessories and components
• Perform functional tests for all complex repair work

Requirements

• Completed technical education, preferably as a motor vehicle technician or a technical education with a comparable qualification, with professional experience or a master's degree
• Communication skills and teamwork
• Experience in digitally supported repair and maintenance
• Knowledge of the use of modern diagnostic methods
• Good German language skills in writing and speaking
• Technical knowledge in the sports car sector
• Strong interest in complex technical relationships and achieving solution-oriented results
• Reliability, sense of responsibility, and willingness to learn
• Independent and diligent working style
• Master's degree
• High commitment and willingness to be part of a highly motivated team
• Class B driving license

Company Profile

In 1950, two weeks before the first Porsche 356 rolled off the production line in Stuttgart, Porsche and the Glöckler family signed a large-scale dealer contract. Otto Glöckler and his son Walter were among the first Porsche dealers worldwide.

Today, the Glöckler Group operates three locations under its umbrella: the Porsche Centre Frankfurt, the Porsche Centre Bad Homburg, and the Porsche Approved & Service Centre Frankfurt West. At all three locations, we are convinced that the success of our company is based on committed and motivated employees.

Our secret to a secure future is the balanced combination of experienced professionals and ambitious young talent. That's why we're always on the lookout for talents who want to take a step up in a dynamic environment – and who share our enthusiasm for the Porsche brand.

Application Notes

We offer:

• Responsible tasks
• Highly qualified and motivated colleagues
• Modern workplaces in a professional working environment
• Continuous training and targeted promotion
• Various career opportunities tailored to your talents
• Employee discounts
• 30 days of holiday

Contact

Please send your application in PDF format to HR:

hr@gloeckler-group.com

For any further questions, please contact:

Karlheinz Stamm, Manager After Sales karlheinz.stamm@porsche-frankfurt.de

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As a Data Center Infrastructure Mechanical Engineer, you will help design, validate, and scale the cooling and mechanical systems that make high-density AI compute possible. You will work across thermal architecture, equipment development, manufacturing validation, construction, commissioning, deployment, and operations, partnering with research, hardware engineering, data center engineering, supply chain, EHS, operations, and external delivery partners.

Key Responsibilities:

• Define mechanical and cooling infrastructure requirements and reference architectures for AI data center campuses, including heat rejection, chilled water, condenser water, liquid cooling distribution, air handling, containment, filtration, water treatment, controls, and metering.

• Review and develop basis-of-design documents, design narratives, P&IDs, mechanical schedules, equipment specifications, thermal capacity models, hydraulic models, CFD analysis, controls sequences, and commissioning requirements.

• Evaluate mechanical architectures for high-density compute, including direct-to-chip liquid cooling, CDU topology, facility water interfaces, manifolds, quick disconnects, hoses, heat exchangers, pumping strategies, leak detection, serviceability, and failure isolation.

• Partner with electrical, controls, hardware, networking, construction, and operations teams to ensure cooling systems support liquid-cooled GPU rack deployments and reliable facility operation.

• Develop technical specifications and acceptance criteria for chillers, cooling towers, dry coolers, evaporative coolers, pumps, heat exchangers, CDUs, manifolds, valves, filters, water treatment systems, CRAHs, air handlers, VFDs, controls panels, sensors, and monitoring devices.

• Lead technical evaluation of equipment vendors, manufacturers, design consultants, commissioning agents, contractors, and testing laboratories; review submittals, P&IDs, control diagrams, performance curves, test reports, certifications, and quality documentation.

• Drive factory acceptance testing, site acceptance testing, witness testing, pressure testing, leak testing, thermal performance testing, reliability testing, interoperability testing, and integrated systems testing for critical equipment and high-density rack deployments.

• Help design and operate a hardware and data center infrastructure R&D laboratory used to validate new cooling equipment, liquid-cooled GPU rack designs, operating envelopes, fault scenarios, telemetry, and facility-hardware interactions.

• Collaborate with hardware manufacturers to evaluate L10 and L11 test procedures, yield, throughput, reliability, serviceability, and readiness for large-scale deployment.

• Define telemetry and controls requirements for mechanical and supporting electrical systems, including temperatures, flow rates, differential pressure, pump status, valve position, chiller status, CDU status, coolant quality, leak detection, alarms, control states, and rack-level thermal signals.

• Analyze lab data, operational incidents, thermal excursions, flow excursions, pressure excursions, leak events, contamination events, nuisance alarms, controls alarms, and rack-level thermal failures to improve designs, procedures, vendor quality, and reliability models.

• Create clear engineering documentation, standards, test procedures, commissioning scripts, operating procedures, decision records, risk registers, and executive-ready technical summaries.

• Provide senior technical escalation support during design reviews, construction, manufacturing validation, commissioning, startup, production deployment, vendor testing, and operational events.

• Mentor partner teams and raise the technical bar for mechanical design, thermal validation, safety, water quality, documentation, reliability, and operational rigor.

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In this role, you'll:

Define and own the strategy, roadmap, and execution for Twilio's SMS and RCS APIs, driving innovation while simplifying the developer experience.

Lead the evolution of our messaging platform, including launching new cross-channel APIs and evolving our legacy APIs at global scale.

Work backwards from developers and enterprises to design intuitive, consistent APIs that power billions of customer interactions

Gather market intelligence, analyze usage patterns, and synthesize insights from prototypes to innovate

Partner closely with engineering, design, GTM, and partner teams to scope, build, and launch capabilities

Drive experimentation through betas and A/B tests, iterating quickly to achieve product–market fit for emerging conversational experiences.

Use data and metrics to identify growth opportunities, performance gaps, and areas to improve reliability.

Mentor and influence teams across the org, championing a developer-first culture in a distributed, remote-first environment.

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio.

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The specific areas / sub-systems of responsibility would be within the base engine / long block and would be defined at the point of employment and joining the base engine team based on the successful candidate’s skill set and experience coupled with the skill set of the existing team. Examples of the ‘base engine’ / long block sub-systems are; Cylinder Block, heads, PCV, EGR, Sealing, Covers, Valvetrain, VCT’s, Cam Drive, Piston and Rod, Rotating system, Oil pump & Lubrication, Engine bearings Etc.

The D&R Engineer is responsible for managing product release CAD and BOM structures, including assembly positioning and mass attribute analysis. This role also includes facilitating supplier data integration and developing CAD studies to support and drive design improvements.

The D&R Engineer is expected to understand and effectively utilize product check-in/check-out processes, version control, data release, and change management within Ford’s PLM ecosystem.

The D&R Engineer will perform feasibility analyses using CAD tools, including draft angle analysis, undercut detection, minimum bend radius validation for sheet metal, and clearance checks to support manufacturing and machining requirements.

Responsibilities:

• Lead/support High Time in Service Quality Projects using Design for Six Sigma / 8D processes on aggressive timelines to support IPTSE’s warranty spend and failure rate reduction targets.
• Responsibilities include evaluation of high warranty spend/failure rate subsystems and engine/vehicle applications with a focus on issues that contribute to > 12 MIS warranty and/or are design/physics related versus supplier quality/manufacturing defect related.
• Projects will be expected to rapidly work through the Define, Characterize, Optimize and Verify phases of the DFSS process including root cause identification and verification, implementation of corrective actions, and prevent recurrence.
• Ability to design, execute or coordinate analytical and physical testing from component bench level to full powertrain/vehicle testing to identify root cause, replicate quality issues and validate fixes.
• Identify and document lessons learned for knowledge preservation.

• Lead new/forward model product development of the base engine sub system for new engine platforms.
• Design, develop, and release components.
• Lead/Support resolution of major design, manufacturing, program, and launch issues as they arise.
• Create, complete, and approve supplier and Ford engine DVPs and oversee testing in support of robust validation.
• Lead Sub System CPMT activities.
• Support all program meetings (EPMT, Design Review, DPMT, change control, etc.).
• Lead drawing release and changes.
• Interface with Calibration, mating components, Purchasing, suppliers, and mfg/assembly plants.
• Prototype Build coordination: load forward model part numbers into system and ensure fit and function.
• Work directly with suppliers, Supplier Technical Assistance (STA), and launch teams to support programs and resolve quality issues, included travel both domestic and international when required.
• WERS (Worldwide engineering release system) coordination; alerts, concerns and notice processing.
• Develop and maintain CPMT documents, including appropriate quality documents (ESOW, DFMEA, PTDV, e3P, SCCAF’s, Global Integrator, PPAP & PSW’s, 543 Chart etc.) for program specific sub-system responsibilities.

Qualifications:

• Bachelor’s of Science Degree in ME/EE/Material Science/Aerospace Engineering.
• 2+ years of experience leading projects.
• 2+ years of experience with internal combustion engines or after-treatment systems in one or more of the following areas: Design, Test, Controls, Calibration.
• Even better, you may have...
• 6-Sigma green belt, black belt and Design for Six Sigma experience a plus.
• Familiarity/experience with Strategic Problem Solving.
• Familiarity/experience with Failure Mode Avoidance and Robustness and Reliability processes
• Familiarity with quality tools.
• Knowledge of key engine/powertrain subsystems including base engine, cooling, airpath, fuel, after-treatment, etc.
• Strong project management, problem solving and organization skills
• Excellent teamwork and communication skills
• Self-starter with the ability to manage multiple work streams simultaneously
• Enjoys taking on new challenges, embraces learning and discovery.

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Key responsibilities include:

• Owning financial data quality end-to-end (consistency, freshness, accuracy) across billing, revenue, and related financial data flows.
• Driving the strategy and execution to bring systems and data capabilities in-house, reducing dependence on external vendors while maintaining operational rigor.
• Partnering with stakeholders across Finance and GTM to define data needs, deliver outputs in expected formats, and ensure data reliability as systems evolve.

You might thrive in this role if you have managed teams that include software engineers who build/modify core systems as well as data-focused engineers,especially in domains where correctness matters.

Additional requirements include:

• Strong SQL and data concepts, and the ability to reason about data modeling, validation, lineage, and reliability.
• Experience with system migrations, reducing vendor dependency, or integrating multiple providers/sources into a consistent set of metrics.
• Effective communication with Finance and GTM stakeholders, translating needs into technical plans and delivering outputs (ie. dashboards, curated tables, exports, APIs, etc.).

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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The Knowledge Work team builds the training environments and evaluations that make Claude effective at real-world professional workflows , searching, analysing, and creating across the tools and documents knowledge workers use every day.

As that work scales, the systems behind it need to be as rigorous as the research itself. We are looking for a Research Engineer to own the reliability, observability, and infrastructure foundation that the team's research depends on.

You will be responsible for ensuring our training and evaluation runs remain stable, well-instrumented, and high-quality as they grow in scale and complexity.

A core part of this role is shifting reliability work from reactive to proactive: hardening systems, stress-testing at realistic scale, and building the observability and tooling that surface problems early , so researchers can stay focused on research rather than incident response.

You will be the team's stable, context-rich owner for environment health and evaluation integrity, and the primary point of contact for partner teams when issues arise.

Key Responsibilities:

• Serve as the dedicated reliability owner for the Knowledge Work training environments, providing continuity of context and reducing the operational overhead of rotating ownership
• Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases
• Build and automate observability, dashboards, and operational tooling for our training environments and evaluation systems, with an emphasis on high signal-to-noise: a small set of trusted metrics and alerts rather than sprawling instrumentation
• Proactively harden environments and evaluation systems through load testing, fault injection, and stress testing at realistic scale, so failures surface early rather than during critical training work
• Act as the primary point of contact for partner training and infrastructure teams when issues in our environments arise, and drive incidents to resolution
• Reduce the operational burden on researchers so they can stay focused on research

Minimum Qualifications:

• Highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production
• Demonstrated experience operating ML or distributed systems at scale, including significant on-call and incident-response experience
• Strong SRE or production-engineering mindset , reaching for SLOs, load tests, and failure injection before reaching for more dashboards
• Foundational ML knowledge sufficient to understand what a training environment or evaluation is actually measuring, and recognise when an evaluation has become stale or gameable
• Able to read research code and reason evaluation integrity

Preferred Qualifications:

• 5+ years of experience operating ML or distributed systems at scale
• Experience building or operating RL environments, agent harnesses, or LLM evaluation frameworks
• Familiarity with reward modelling, evaluation design, or detecting and mitigating reward hacking
• Experience with observability stacks (metrics, tracing, structured logging) and operational dashboard tooling
• Background in chaos engineering, fault injection, or large-scale load testing
• Experience with data quality pipelines, drift detection, or evaluation-set curation and versioning
• Familiarity with large-scale training or inference infrastructure (schedulers, multi-agent orchestration, sandboxed execution)
• Prior experience as a dedicated reliability or operations owner embedded within a research team

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren’t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we’re different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits.

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In this role, you'll think about queries and RPCs in terms of syscalls, cache lines, and wire formats, and design systems that stay fast and predictable under load. You'll help define and harden the foundation for our training and serving stack: from storage and indexing strategies, to streaming and fanout, to backpressure and failure handling across services and regions.

You'll work closely with software engineering, data infra, and SRE partners to ensure our systems are observable, debuggable, and operable in production. If topics like IO scheduling and batching, lock-free or low-contention data structures, connection pooling, query planning, kernel and network tuning, on-disk layout and indexing, circuit-breaking, autoscaling, incident response, NixOS, Rust, and robust SLIs/SLOs sound interesting (even if it's just a subset), this role gives you a chance to apply that expertise to business-critical, high-leverage infrastructure at Pinterest scale.

What you'll do:

• Scale the decision making process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments

• Improve the developer experience for the data science team

• Upgrade our observability tooling

• Make every deployment smooth as our infrastructure evolves

What we're looking for:

• Deep understanding of Linux

• Excellent writing skills

• A systems-oriented mindset

• Experience in high-performance software (RTB, HFT, etc.)

• Software engineering experience + reliability (e.g. CI/CD) expertise

• Strong observability instincts

• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs

• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)

• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables

Nice-To-Haves:

• Reverse-engineering experience

• Terraform, EKS, or MLOps experience

• Python, Scala, or Zig experience

• NixOS experience

• Adtech or CTV experience

• Experience deploying a distributed system across multiple clouds

• Experience in hard real-time low-latency

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As an Engineering Manager for the APAC & EMEA Cards team at Stripe, you will be responsible for expanding the reach of Stripe's global payments network, delivering the best-in-class experience for card payments in Asia Pacific, Europe, and the Middle East regions.

Responsibilities

• Work with other Stripe leaders to author Stripe's card payments strategy in APAC & EMEA
• Develop engineers on the team, helping them advance in their careers
• Empower the engineering team to achieve a high level of technical productivity, reliability, and simplicity
• Manage processes to help the team do its best work and interface effectively with the rest of Stripe
• Contribute to engineering-wide initiatives as a member of Stripe's engineering management team
• Recruit great engineers, in collaboration with Stripe's recruiting team

Requirements

• BS or MS in Computer Science or equivalent technical field
• 3+ years of Engineering Management experience
• 7+ years of experience in a Software Engineering role
• Demonstrated ability to define and drive complex initiatives/projects
• Experience hiring top talent, building diverse, high-performing teams
• Strong ability to communicate both with technical and non-technical audiences

Salary

Stripe offers a competitive salary and benefits package.

Location

This role is based in Singapore.

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As a Guest Host, you will be responsible for ensuring a professional customer contact and exclusive customer care at the Porsche Centre. You will represent the Porsche Centre, even in your role as Guest Host, and be involved in the exciting daily planning with an overview of workshop events and customers.

You will create an exclusive Porsche experience through excellent hospitality by offering refreshments, magazines, seating areas, and more. You will also guide customers to service, sales, or waiting areas and ask about their wishes and personally bid farewell.

You will support the Service Assistant with daily tasks. Experience in this area would be helpful.

Requirements:

• Service-oriented and excellent communication skills
• Good orientation skills with a sense of personal care
• Experience in Customer Relations with premium segment vehicles
• Excellent manners
• High motivation and flexible working hours
• Independent and meticulous work style
• Reliability and organisational skills
• Good foreign language skills in English

Working at our company means:

• A secure job in a future-oriented company
• Varied and challenging tasks
• Fair and attractive remuneration including special payments and voluntary social benefits
• Individual opportunities for further education
• Contributions to pension provision
• Bike leasing
• Employee events (celebration events, employee festival, etc.)
• 'Headhunter' , employees recruit employees

If you're interested, please send your complete application in a PDF file to bewerbung@seitz-gruppe.de.

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In this role, you will specialise in backend development, designing and optimising robust microservices to ensure scalability, security, and reliability. You will support full-stack efforts, collaborate with cross-functional teams on payments, fraud detection, and compliance initiatives, and contribute to the creation of a high-scale financial products platform.

Responsibilities:

• Develop and optimise microservices for high-concurrency transactions using Go, Postgres, and Kafka.
• Collaborate on system architecture, testing, and monitoring to ensure uptime and performance.
• Build internal tools using frontend technologies as needed to support operational efficiency.
• Support the Technical Lead in risk mitigation and align with engineering, product, and compliance teams to drive project success.
• Contribute to the development of secure, scalable systems for handling financial data and transactions.
• Iterate rapidly on feedback to deliver high-quality solutions in a dynamic environment.

Basic Qualifications:

• 5+ years of software engineering experience, with a strong focus on backend development.
• Proficiency in Go or similar languages and experience with databases (e.g., Postgres) and streaming systems (e.g., Kafka).
• Familiarity with building distributed systems for high-scale, low-latency environments.
• Knowledge of handling secure financial data.
• Ability to contribute to frontend development for internal tools when required.
• Strong communication and problem-solving skills, with a collaborative mindset.

Preferred Skills and Experience:

• Experience in fintech, payments, or regulatory frameworks (e.g., PCI-DSS, AML/KYC).
• Prior work in a fast-paced, startup-like environment on greenfield projects.
• Comfort navigating ambiguous requirements and iterating based on feedback.
• Passion for leveraging AI to transform financial systems.

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Key responsibilities include:

• Scaling the decision-making process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments
• Improving the developer experience for the data science team
• Upgrading our observability tooling
• Serving as a technical lead and mentor to the team
• Making every deployment smooth as our infrastructure evolves

Requirements include:

• Deep understanding of Linux
• Excellent writing skills
• A systems-oriented mindset
• Experience in high-performance software (RTB, HFT, etc.)
• Software engineering experience + reliability (e.g. CI/CD) expertise
• Strong observability instincts
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables

Nice-to-haves include:

• Reverse-engineering experience
• Terraform, EKS, or MLOps experience
• Python, Scala, or Zig experience
• NixOS experience
• Adtech or CTV experience
• Experience deploying a distributed system across multiple clouds
• Experience in hard real-time low-latency

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This role is essential for keeping our most contended infrastructure teams shipping effectively while Research, Product, and Safety all depend on their output.

Responsibilities:

• Systems Integration & Coordination: Lead cross-functional initiatives for new infrastructure integration, establishing clear ownership, timelines, and communication channels between teams. Drive end-to-end planning for major infrastructure transitions including platform modernization and new tech adoption.

• Performance & Efficiency: Partner with engineering teams to identify optimisation opportunities, track performance metrics, and prioritise work that unlocks capacity gains. Coordinate across runtime and accelerator layers to ensure efficiency wins ship without compromising reliability.

• Launch Coordination: Drive end-to-end readiness for model and feature launches across multiple hardware platforms. Establish processes for cross-platform validation, manage launch timelines, and ensure smooth handoffs between runtime, accelerator, and downstream teams.

• Strategic Planning: Own and prioritise the inference deployment roadmap, working closely with engineering leadership to prioritise initiatives and manage dependencies. Provide visibility into upcoming changes and their organisational impact.

• Stakeholder Communication: Build strong relationships across research, engineering, and product teams to understand requirements and constraints. Translate technical complexities into clear updates for leadership and ensure alignment on priorities and timelines.

• Process Improvement: Identify inefficiencies in current workflows and drive systematic improvements. Establish metrics and dashboards to track infrastructure health, capacity utilisation, and deployment success rates.

You may be a good fit if you:

• Have several years of experience in technical program management, with proven success delivering complex infrastructure programs, preferably in ML/AI systems or large-scale distributed systems

• Have deep technical understanding of inference systems, compilers, or hardware accelerators to engage substantively with engineers and identify technical risks.

• Excel at creating structure and processes in ambiguous environments, bringing clarity to complex cross-team initiatives

• Have strong stakeholder management skills and can build trust with both technical and non-technical partners

• Are comfortable navigating competing priorities and using data to drive technical decisions

• Have experience with infrastructure scaling initiatives, hardware integrations, or deployment governance

• Thrive in fast-paced environments and can balance strategic planning with tactical execution

• Are passionate about AI infrastructure and understand the unique challenges of deploying and scaling large language models

Deadline to apply: None, applications will be received on a rolling basis.

The annual compensation range for this role is $290,000-$365,000 USD.

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You will work closely with teams across Anthropic to improve reliability across our most critical serving paths. You will be responsible for making the systems that deliver Claude more robust and resilient, whether during an incident or collaborating on projects.

To be successful in this role, you should have strong distributed systems, infrastructure, or reliability backgrounds. You should be curious and brave, comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.

You will be working on high-availability serving infrastructure across multiple regions and cloud providers. You will support the reliability of safeguard model serving, which is critical for both site reliability and Anthropic's safety commitments.

If you're committed to creating reliable, interpretable, and steerable AI systems, and you're passionate about working on complex technical problems, we'd love to hear from you.

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You may be a good fit for this role if you have strong distributed systems, infrastructure, or reliability backgrounds, are curious and brave, think holistically about how systems compose and where the seams are, can build lasting relationships across teams, care about users and feel ownership over outcomes, have excellent communication and collaboration skills, and bring diverse experience.

Strong candidates may also have experience operating large-scale model serving or training infrastructure, experience with one or more ML hardware accelerators, understanding of ML-specific networking optimizations, expertise in AI-specific observability tools and frameworks, experience with chaos engineering and systematic resilience testing, and contributions to open-source infrastructure or ML tooling.

We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. We value impact and believe that the highest-impact AI research will be big science. We work as a single cohesive team on just a few large-scale research efforts and value communication skills.

If you're interested in this role, please submit an application even if you don't believe you meet every single qualification. We encourage diversity and strive to include a range of diverse perspectives on our team.

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Key responsibilities include:

• Designing and implementing distributed storage solutions to support scaling data-intensive AI workloads
• Contributing to the development of exabyte-scale, S3-compatible object storage
• Integrating dedicated storage clusters into diverse customer environments
• Working with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency
• Leading efforts to improve the reliability, durability, security, and observability of the storage stack
• Collaborating with operations teams to monitor, troubleshoot, and improve storage systems in production environments
• Setting the bar for developing metrics and dashboards to provide visibility into storage performance and health
• Analyzing telemetry and system data to drive improvements in throughput, latency, and resilience
• Working cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack
• Sharing knowledge and mentoring other engineers on best practices in building distributed, high-performance systems

Requirements include:

• Bachelor's, Master's, or PhD degree in Computer Science, Engineering, or a related field
• 8-10+ years of experience working in storage systems engineering or infrastructure
• Strong hands-on experience with object storage or distributed filesystems in production environments
• Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar
• Proficiency in a systems programming language such as Go, C, or Rust
• Proficiency leveraging AI tools to augment software development
• Familiarity with storage observability tools and telemetry pipelines (e.g., ClickHouse, Prometheus, Grafana)
• Experience working with cloud-native infrastructure, Kubernetes, and scalable system architectures

The base salary range for this role is $188,000 to $275,000.

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In addition to a competitive salary, we offer a variety of benefits to support your needs, including medical, dental, and vision insurance, company-paid life insurance, voluntary supplemental life insurance, short and long-term disability insurance, flexible spending account, health savings account, tuition reimbursement, ability to participate in employee stock purchase program (ESPP), mental wellness benefits through Spring Health, family-forming support provided by Carrot, paid parental leave, flexible, full-service childcare support with Kinside, 401(k) with a generous employer match, flexible PTO, catered lunch each day in our office and data center locations, and a casual work environment.

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This is a high-leverage role. The tools you build will be used by dozens of researchers and investigators, and directly shape our ability to measure and mitigate both misuse and misalignment.

You'll work across the full stack, from core analysis frameworks through user-facing apps and interfaces.

Responsibilities:

• Design and implement AI-based monitoring systems for AI training and deployment
• Extend and improve core frameworks for processing large volumes of unstructured text
• Partner with researchers and safety teams across Anthropic to understand their analytical needs and build solutions
• Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings
• Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest

You May Be a Good Fit If You:

• Have 5+ years of software engineering experience, with meaningful exposure to ML systems
• Are excited about the problem of scaling human oversight of AI systems
• Are familiar with LLM application development and evaluation
• Enjoy building tools that other people use , you care about UX, reliability, and documentation
• Thrive in collaborative, cross-functional environments

Strong Candidates May Also Have:

• Experience with productionizing internal tools or building developer-facing platforms
• Background in building monitoring or observability systems
• Comfort with ambiguity , our team is small and growing, and you'll help define what we become

The annual compensation range for this role is $320,000-$405,000 USD.

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This role is perfect for an engineer with a solid foundation in power systems, embedded systems who is eager to grow, own challenging projects, and see their work move rapidly from concept to flight. If you thrive in fast-paced environments, enjoy solving complex problems, and want to help build the future of autonomous aviation, we want to hear from you.

Responsibilities:

• Lead failure analysis investigations for avionics hardware, including: PCBAs (digital, analog, power, mixed-signal), harnesses, connectors, and interconnects
• Perform root cause analysis (RCA) using structured methodologies (8D, 5-Why, Fishbone, Fault Tree, etc.)
• Conduct and/or oversee electrical, mechanical, and environmental failure analysis
• Collaborate with design engineers to identify design vulnerabilities, manufacturing and suppliers on process-related issues
• Work closely with cross-functional teams (reliability, mechanical, embedded, software, production, supply chain, etc.)
• Drive design, process, and supplier corrective actions to closure
• Author clear, data-driven failure analysis reports and present findings to technical and program leadership
• Mentor junior engineers and help establish best practices for failure analysis and reliability

Required qualifications:

• Bachelor’s degree in electrical engineering; or equivalent work experience
• 8+ years of experience in failure analysis, reliability, or hardware debug for aerospace, defense, or high-reliability electronics
• Strong technical fundamentals
• Experienced with cross-sectioning, microscopy, X-ray, and other FA techniques
• Strong hands-on experience with PCBA failure mechanisms (solder joints, vias, components, PCB materials), Harnesses, connectors, crimping, shielding, and interconnect reliability
• Demonstrated track record of extreme ownership, working hard, and delivering outstanding outcomes
• A track record of successfully delivery of products and solutions, overcome obstacles, and deliver high-quality solutions to meet project deadlines and exceed expectations in a fast-paced environment
• Proactively identify opportunities for improvement within organization, demonstrating initiative to propose and implement innovative solutions that enhance efficiency, quality, and overall project success and aircraft reliability
• Ability to independently drive investigations from symptom to root cause

$150,000 - $220,000 a year

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This role is perfect for an engineer with a solid foundation in power systems and embedded systems who is eager to grow, own challenging projects, and see their work move rapidly from concept to flight. If you thrive in fast-paced environments, enjoy solving complex problems, and want to help build the future of autonomous aviation, we want to hear from you.

Key responsibilities include:

Performing failure analysis investigations for avionics hardware, including PCBAs, harnesses, connectors, and interconnects Conducting root cause analysis Collaborating with design engineers to identify design vulnerabilities, manufacturing, and suppliers on process-related issues Working closely with cross-functional teams (reliability, mechanical, embedded, software, production, supply chain, etc.) Driving design, process, and supplier corrective actions to closure

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About the company: The mining industry has steadily become worse at finding new ore deposits, requiring >10X more capital to make discoveries compared to 30 years ago. KoBold Metals builds AI models for mineral exploration and deploys those models,alongside our novel sensors,to guide decisions on KoBold-owned-and-operated exploration programs.

About The Role: In this role, you will partner with exploration and engineering teams to build reliable, scalable infrastructure that makes it easier to turn data and models into real-world exploration insights. You will improve observability, streamline MLOps workflows, and maintain shared tools like JupyterHub that enable faster experimentation and collaboration. Your work will help create a solid foundation for scientists and engineers to focus on discovery instead of infrastructure.

Responsibilities

• Design, build, and operate compute infrastructure that is both scalable and reliable to support critical services.
• Work closely with engineering teams to embed observability, reliability, and security throughout the software development process.
• Create and maintain automation for monitoring, deployments, and incident response to keep operations efficient and predictable.
• Lead or support capacity planning, performance reviews, and system tuning to ensure stable and efficient systems.
• Join the on-call rotation and take part in incident response, troubleshooting, and resolution.
• Develop and refine monitoring and alerting to catch issues early and reduce downtime.
• Establish and maintain disaster recovery and business continuity practices that protect the organization against failures.
• Regularly review and improve our tools and processes to strengthen system visibility and reliability.
• Investigate points of fragility in distributed systems and understand how complex systems behave under stress in order to improve resilience.
• Continually learn about mineral exploration through reading, discussions with exploration team members, periodic rotation on an exploration team and time in the field with geologists

Qualifications

• 5+ years of experience as an Infrastructure Engineer, Site Reliability Engineer or in a similar role
• Strong scripting and programming skills (Python, Go, Java or JavaScript/ Node.js )
• Experience with IaC tools like Terraform and container orchestration tools like Kubernetes and Docker
• Experience with cloud platforms such as AWS
• Experience operating or administering JupyterHub in a multi-user environment
• Understanding of MLOps workflows, including model training, deployment, and related tooling
• Excellent communication & collaboration skills and a continuous improvement mindset
• Proven ability to troubleshoot complex issues and implement effective solutions
• Proven ability to thrive in dynamic and evolving environments, effectively navigating uncertainty and incomplete information.
• Proven ability to grow expertise, influence & educate others
• Comfortable making informed decisions with limited data, adapting quickly to new circumstances, and maintaining focus on strategic objectives while driving clarity for the team.
• Intellectual curiosity and eagerness to learn about all aspects of mineral exploration, particularly in the geology domain. Enjoys constantly learning such that you are driving insights through using our tools in exploration and willing to work directly with geologists in the field.
• Ability to explain technical problems to and collaborate on solutions with domain experts who are not infrastructure engineers. A strong communicator who enjoys working with colleagues across the company.
• Excitement about joining a fast-growing early-stage company, comfort with a dynamic work environment, and eagerness to take on an evolving range of responsibilities.
• Keen not just to build cool technology, but to figure out what technical product to build to best achieve the business objectives of the company.

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We are building infrastructure that enables the world's largest financial institutions,banks, broker-dealers, fintechs, and payments companies,to offer digital asset services to their end clients.

This is a greenfield opportunity to architect and build a platform that will power the next generation of institutional digital asset adoption.

As an engineer on this team, you will drive the technical direction of our infrastructure services platform, mentor other engineers, and help shape the product roadmap.

Technical Skills:

• Build secure, scalable infrastructure that powers digital asset custody, trading, staking, and settlement for enterprise financial institutions
• Design and implement virtual accounting layers, API integrations, and multi-tenant platform architectures
• Drive technical excellence through code reviews, design specs, and mentorship across the engineering organization
• Foster an efficient testing culture with emphasis on security, reliability, and minimizing tech debt in a regulated environment

Complexity and Impact of Work:

• Lead complex cross-functional projects spanning custody, trading, compliance, and client integration
• Break down large initiatives into well-scoped deliverables; accurately estimate multi-person, multi-quarter projects
• Own the end-to-end lifecycle of platform features,from architecture through production support and on-call rotation
• Find the right balance between shipping velocity and the perfection required for regulated financial infrastructure

Organizational Knowledge:

• Collaborate across product lines and with enterprise clients to understand and deliver on their infrastructure needs
• Work closely with compliance, legal, and security teams to ensure platform meets regulatory requirements
• Contribute to scaling the team through hiring, onboarding, and knowledge sharing

Communication and Influence:

• Influence architecture decisions and product roadmap,have a seat at the table with leadership
• Mentor and guide engineers on the team; help others connect their work to Anchorage's strategic goals
• Communicate effectively with external stakeholders, including enterprise clients and partners

You may be a fit for this role if you have:

• 8+ years of professional experience building backend services and distributed systems
• Experience designing and delivering customer-facing APIs and multi-tenant platform architectures
• Experience with cloud-native microservices (we use GCP, but experience with other cloud providers is sufficient)
• A track record of building automated tests alongside your features and maintaining production systems over time
• Experience performing on-call support and incident response for production systems
• Strong computer science fundamentals (algorithms, data structures, systems design),formal CS degree not required
• A genuine interest in code quality, infrastructure reliability, and security
• The ability to prioritize end-user experience and business value over 'cool tech'

Although not a requirement, bonus points if:

• Professional experience with C++, Go, or Rust
• Professional experience with Kubernetes and container orchestration
• Experience building ledger systems, virtual accounting layers, or custody infrastructure
• Background in financial services, fintech, or banking-as-a-service platforms
• Experience with system security (authentication, authorization, identity management, API keys)
• Familiarity with regulatory requirements for financial institutions (SOC 2, bank examinations, etc.)
• Background in the cryptocurrency or digital assets industry
• You were emotionally moved by the soundtrack to Hamilton, which chronicles the founding of a new financial system.

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at ABM UK

On-site Dyce, Scotland, United Kingdom B&I - Scotland & Ireland (Jenna Pollock) Full-time

Posted 6 months ago

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Description

LOCATION: Aberdeen Airport

HOURS: 37.5 hours per week, 5 over 7, varied shifts

PAY RATE: £12.21 per hour

_If you require any additional support or adjustments during the recruitment process, please don't hesitate to contact our Recruitment Department at recruitment@abm.com . We're here to help!_

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We are looking for someone with a good secondary school leaving certificate or equivalent, who is proficient in German in writing and speaking. First-hand knowledge of MS Office is desirable. You should be interested in office organisation and personnel management. Good English skills are also required. You should have excellent team and communication skills and a structured and reliable way of working.

This position is also suitable for people with disabilities.

We offer a dynamic and innovative work environment, where you will be integrated into our team through our onboarding process. We value lifelong learning and encourage you to take the initiative. We also offer flexible working hours, mobile working, and a modern workplace.

Our values are respect, communication, care, openness, passion, and trust, which we live out in our daily interactions.

If you are interested in this opportunity, we would be happy to get to know you better!

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

Our mandate is to make inference deployment boring and unattended.

Anthropic serves Claude to millions of users across GPUs, TPUs, and Trainium — and every model update must reach production safely, quickly, and without disrupting service. We're building the systems that make inference deployment continuous and unattended.

As a Software Engineer on the Launch Engineering team, you'll design and build the deployment infrastructure that moves inference code from merge to production. This is a resource-constrained optimization problem at its core: validation and deployment consume the same accelerator chips that serve customer traffic — your deploys compete with live user requests for the same hardware. Every model brings different fleet sizes, startup times, and correctness requirements, so the system must adapt continuously. You'll build systems that navigate these constraints — orchestrating validation, scheduling deployments intelligently, and driving down cycle time from merge to production.

If you've built deployment systems at scale and gravitate toward the hardest problems at the intersection of automation and resource management, this team will give you an outsized scope to work on them.

Responsibilities

• Own deployment orchestration that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets, unattended under normal conditions
• Improve capacity-aware deployment scheduling to maximize deployment throughput against constrained accelerator budgets and variable fleet sizes
• Extend deployment observability — dashboards and tooling that answer "what code is running in production," "where is my commit," and "what validation passed for this deploy"
• Drive down cycle time from code merge to production with pipeline architectures that minimize serial dependencies and maximize parallelism
• Optimize fleet rollout strategies for large-scale deployments across thousands of GPU, TPU, and Trainium chips, minimizing disruption to serving capacity
• Evolve self-service model onboarding so that new models can be added to the continuous deployment pipeline without Launch Engineering involvement
• Partner across the Inference organization with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems

You May Be a Good Fit If You Have

• 5+ years of experience building deployment, release, or delivery infrastructure at scale
• Strong software engineering skills with experience designing systems that manage complex state machines and multi-stage pipelines
• Experience with deployment systems where resource constraints shape the design — whether that's fleet capacity, network bandwidth, hardware availability, or coordinated rollout windows
• A track record of building automation that measurably improves deployment velocity and reliability
• Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration
• Comfort working across the stack — from backend services and databases to CLI tools and web UIs
• Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners

Strong Candidates May Also Have

• Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium)
• Background in capacity planning or resource-constrained scheduling (e.g., bin-packing, fleet management, job scheduling with hardware affinity)
• Experience with progressive delivery in systems with long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback
• Experience at companies with large-scale release engineering challenges (mobile release trains, monorepo deployments, multi-datacenter rollouts)
• Experience with Python and/or Rust in production systems

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role:

Claude.ai is Anthropic's flagship consumer product, spanning web, mobile, desktop, and browser extensions—and it's growing fast. The UI Platform team owns the foundational layer that makes all of this possible: the shared infrastructure, components, APIs, and tooling that let every other product team at Anthropic ship quickly, reliably, and with confidence.

As a Software Engineer on this team, you'll be hands-on building the platform that other engineers depend on every day. This means designing and shipping shared components and design-system-level abstractions, evolving the backend-for-frontend (BFF) APIs that power our client applications, and improving the build, deploy, and observability systems that keep Claude.ai running smoothly across surfaces. Your work will directly shape how fast teams across Anthropic can innovate—and how stable their experiences are when they do.

This is a great fit if you care deeply about developer experience and want your engineering work to have outsized leverage: instead of shipping one feature, you're building the tools and systems that make dozens of features possible.

Responsibilities:

• Design and build shared UI components, libraries, and abstractions that product teams across Anthropic use to ship consistently and efficiently on web and mobile
• Contribute to the BFF API layer that powers Claude.ai's client applications—thinking carefully about clean contracts, performance, and reliability at the boundary between frontend and backend
• Improve developer velocity across the organisation by reducing friction in our build, deploy, and testing pipelines
• Work on performance and reliability: identify and resolve latency issues, improve observability, and help establish high standards that the rest of the platform team can build on
• Partner closely with product engineering teams to understand their needs, unblock them when possible, and shape platform investments around where the most impact is
• Help maintain and evolve documentation and tooling that make the platform approachable for engineers joining or building on top of it

You may be a good fit if you:

• Have 5+ years of software engineering experience, with significant time spent building shared platforms, developer tools, or infrastructure that other engineers rely on
• Have strong practical skills in modern web technologies (React, TypeScript, Next.js) and experience designing or consuming APIs that serve frontend applications
• Care about developer experience and have a track record of building things that make other engineers more productive
• Have solid instincts around reliability, observability, and performance—and enjoy operationalising those instincts in production systems
• Thrive in fast-paced, collaborative environments and enjoy working closely with cross-functional partners
• Pick up slack, even if it goes outside your job description

Strong candidates may also have experience with:

• Building shared component libraries or design systems for multiple surfaces (web, mobile, desktop)
• BFF architectures and API patterns that balance flexibility with consistency across client platforms
• Performance optimisation and latency reduction in consumer-facing applications
• CI/CD, build systems, and deployment automation
• Observability and monitoring (metrics, logging, tracing)
• Working on AI/ML products or in rapidly evolving product environments

Candidates need not have:

• 100% of the skills needed to perform the job
• Formal certifications or education credentials

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

Claude has your back. AIRE has Claude's. Help us keep Claude reliable for everyone who depends on it.

AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.

Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.

Responsibilities

• Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.
• Design and implement monitoring and observability systems across the token path.
• Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers
• Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.
• Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.

You may be a good fit if you

• Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs.
• Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.
• Think holistically about how systems compose and where the seams are.
• Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.
• Care about users and feel ownership over outcomes, even for systems you don't own.
• Have excellent communication and collaboration skills -- you'll be partnering across the entire company.
• Bring diverse experience -- the team's strength comes from people who've built product stacks, scaled databases, run massive distributed systems, and everything in between.

Strong candidates may also

• Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems
• Have experience operating large-scale model serving or training infrastructure (>1000 GPUs).
• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).
• Understand ML-specific networking optimizations like RDMA and InfiniBand.
• Have expertise in AI-specific observability tools and frameworks.
• Have experience with chaos engineering and systematic resilience testing.
• Have contributed to open-source infrastructure or ML tooling.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship

We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification.

Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us.

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and engineering as it does with computer science.

XML job scraping automation by YubHub

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.

Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.

Claude has your back. AIRE has Claude's. Help us keep Claude reliable for everyone who depends on it.

Responsibilities:

• Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.

• Design and implement monitoring and observability systems across the token path.

• Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers

• Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.

• Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.

You may be a good fit if you:

• Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs.

• Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.

• Think holistically about how systems compose and where the seams are.

• Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.

• Care about users and feel ownership over outcomes, even for systems you don't own.

• Have excellent communication and collaboration skills -- you'll be partnering across the entire company.

• Bring diverse experience -- the team's strength comes from people who've built product stacks, scaled databases, run massive distributed systems, and everything in between.

Strong candidates may also:

• Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems

• Have experience operating large-scale model serving or training infrastructure (>1000 GPUs).

• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).

• Understand ML-specific networking optimizations like RDMA and InfiniBand.

• Have expertise in AI-specific observability tools and frameworks.

• Have experience with chaos engineering and systematic resilience testing.

• Have contributed to open-source infrastructure or ML tooling.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as a team sport, where everyone contributes to the overall success of the team.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Team

As AI training and deployments scale, the volume of data we need to monitor and understand is exploding. Our team uses Claude itself to make sense of this data. We own an integrated set of tools enabling Anthropic to ask open-ended questions, surface unexpected patterns, and maintain meaningful human oversight over massive datasets.

Our tools are widely adopted internally — powering ongoing enforcement, threat intelligence investigations, model audits, and more — and we’re looking for experienced engineers and researchers to both scale up existing applications and go zero-to-one on new ones.

About the Role

As a Research Engineer on our team, you'll design and build systems that let AI analyse large, unstructured datasets — think tens or hundreds of thousands of conversations or documents — and produce structured, trustworthy insights. You'll work across the full stack, from core analysis frameworks through user-facing apps and interfaces.

This is a high-leverage role. The tools you build will be used by dozens of researchers and investigators, and directly shape our ability to measure and mitigate both misuse and misalignment.

Responsibilities:

• Design and implement AI-based monitoring systems for AI training and deployment

• Extend and improve core frameworks for processing large volumes of unstructured text

• Partner with researchers and safety teams across Anthropic to understand their analytical needs and build solutions

• Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings

• Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest

You May Be a Good Fit If You:

• Have 5+ years of software engineering experience, with meaningful exposure to ML systems

• Are excited about the problem of scaling human oversight of AI systems

• Are familiar with LLM application development (context engineering, evaluation, orchestration)

• Enjoy building tools that other people use — you care about UX, reliability, and documentation

• Can context-switch between deep infrastructure work and user-facing product thinking

• Thrive in collaborative, cross-functional environments

Strong Candidates May Also Have:

• Research experience in AI safety, alignment, or responsible deployment

• Practical experience with both data science and engineering, including developing and using large-scale data processing frameworks

• Experience with productionizing internal tools or building developer-facing platforms

• Background in building monitoring or observability systems

• Comfort with ambiguity — our team is small and growing, and you'll help define what we become

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work.

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Responsibilities:

• Systems Integration & Coordination: Lead cross-functional initiatives for new infrastructure integration, establishing clear ownership, timelines, and communication channels between teams. Drive end-to-end planning for major infrastructure transitions including platform modernization and new tech adoption.
• Performance & Efficiency: Partner with engineering teams to identify optimisation opportunities, track performance metrics, and prioritise work that unlocks capacity gains. Coordinate across runtime and accelerator layers to ensure efficiency wins ship without compromising reliability.
• Launch Coordination: Drive end-to-end readiness for model and feature launches across multiple hardware platforms. Establish processes for cross-platform validation, manage launch timelines, and ensure smooth handoffs between runtime, accelerator, and downstream teams.
• Strategic Planning: Own and prioritise the inference deployment roadmap, working closely with engineering leadership to prioritise initiatives and manage dependencies. Provide visibility into upcoming changes and their organisational impact.
• Stakeholder Communication: Build strong relationships across research, engineering, and product teams to understand requirements and constraints. Translate technical complexities into clear updates for leadership and ensure alignment on priorities and timelines.
• Process Improvement: Identify inefficiencies in current workflows and drive systematic improvements. Establish metrics and dashboards to track infrastructure health, capacity utilisation, and deployment success rates.

You may be a good fit if you:

• Have several years of experience in technical program management, with proven success delivering complex infrastructure programs, preferably in ML/AI systems or large-scale distributed systems
• Have deep technical understanding of inference systems, compilers, or hardware accelerators to engage substantively with engineers and identify technical risks.
• Excel at creating structure and processes in ambiguous environments, bringing clarity to complex cross-team initiatives
• Have strong stakeholder management skills and can build trust with both technical and non-technical partners
• Are comfortable navigating competing priorities and using data to drive technical decisions
• Have experience with infrastructure scaling initiatives, hardware integrations, or deployment governance
• Thrive in fast-paced environments and can balance strategic planning with tactical execution
• Are passionate about AI infrastructure and understand the unique challenges of deploying and scaling large language models

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role:

Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.

This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimisation, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow.

Responsibilities:

• Own critical aspects of our production pretraining pipeline, including model operations, performance optimisation, observability, and reliability
• Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
• Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
• Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
• Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
• Add new capabilities to the training codebase, such as long context support or novel architectures
• Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
• Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned

You May Be a Good Fit If You:

• Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
• Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
• Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
• Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
• Excel at debugging complex, ambiguous problems across multiple layers of the stack
• Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
• Are passionate about the work itself and want to refine your craft as a research engineer
• Care about the societal impacts of AI and responsible scaling

Strong Candidates May Also Have:

• Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
• Contributed to open-source LLM frameworks (e.g., open\_lm, llm-foundry, mesh-transformer-jax)
• Published research on model training, scaling laws, or ML systems
• Experience with production ML systems, observability tools, or evaluation infrastructure
• Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence

What Makes This Role Unique:

This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.

However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.

We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

Claude has your back. AIRE has Claude's. Help us keep Claude reliable for everyone who depends on it.

AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.

Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.

Responsibilities

• Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.
• Design and implement monitoring and observability systems across the token path.
• Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers
• Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.
• Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.

You may be a good fit if you

• Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs.
• Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.
• Think holistically about how systems compose and where the seams are.
• Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.
• Care about users and feel ownership over outcomes, even for systems you don't own.
• Have excellent communication and collaboration skills -- you'll be partnering across the entire company.
• Bring diverse experience -- the team's strength comes from people who've built product stacks, scaled databases, run massive distributed systems, and everything in between.

Strong candidates may also

• Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems
• Have experience operating large-scale model serving or training infrastructure (>1000 GPUs).
• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).
• Understand ML-specific networking optimizations like RDMA and InfiniBand.
• Have expertise in AI-specific observability tools and frameworks.
• Have experience with chaos engineering and systematic resilience testing.
• Have contributed to open-source infrastructure or ML tooling.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Salary

The annual compensation range for this role is €235.000 - €295.000EUR.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and engineering as it does with computer science. We strive to build a team that reflects this perspective, with people from a wide range of backgrounds and disciplines.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role:

Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.

This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow.

Responsibilities:

• Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability
• Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
• Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
• Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
• Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
• Add new capabilities to the training codebase, such as long context support or novel architectures
• Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
• Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned

You May Be a Good Fit If You:

• Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
• Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
• Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
• Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
• Excel at debugging complex, ambiguous problems across multiple layers of the stack
• Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
• Are passionate about the work itself and want to refine your craft as a research engineer
• Care about the societal impacts of AI and responsible scaling

Strong Candidates May Also Have:

• Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
• Contributed to open-source LLM frameworks (e.g., open\_lm, llm-foundry, mesh-transformer-jax)
• Published research on model training, scaling laws, or ML systems
• Experience with production ML systems, observability tools, or evaluation infrastructure
• Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence

What Makes This Role Unique:

This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.

However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred. For people who thrive on this type of work, it's uniquely rewarding.

We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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As an Engineer on the Client Infrastructure team at Cursor, you'll design and build the systems that make our desktop experience performant and stable for millions of developers.

You'll work across the core architecture of the Cursor client—from build and distribution systems to performance instrumentation and shared abstractions that other teams rely on. The work is deeply technical and central to the company's success. Developers expect near-perfect performance and reliability from their trusted tools; your work will make that possible.

Cursor's client is growing fast, spanning macOS, Windows, and Linux, and serving an increasingly diverse user base of professional engineers across many programming languages and developer ecosystems. You'll shape the systems that let us ship safely and quickly, while pushing the limits of what's possible in an AI-driven development environment.

Example projects include...

• Building core client infrastructure: shared abstractions, build systems, and observability foundations.

• Improving reliability and performance: eliminating memory leaks, crashes, and frame-time inconsistencies.

• Designing instrumentation and telemetry systems for profiling, structured logging, and performance measurement.

• Partnering with product teams to unlock faster release velocity—shipping updates to users daily.

• Evaluating Electron vs. native approaches for critical code paths, guiding architectural tradeoffs.

• Building internal tooling that enables other engineers to ship safely and quickly on the client platform.

You may be a fit if

• You have deep experience in client infrastructure—build systems, performance, distribution, or observability.

• You've built or contributed to high-performance desktop applications, whether Electron-based or native.

• You care deeply about performance, reliability, and developer experience, and have an eye for elegant systems design.

• You thrive in environments that are fast-moving, collaborative, and product-focused.

• You're excited to push the limits of what's possible in AI-driven developer tools.

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Platform Engineer, Forward Deployed Engineering (FDE) - NYC

Location

New York City

Employment Type

Full time

Location Type

Hybrid

Department

Model Deployment for Business

Compensation

• $230K – $385K

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the team

OpenAI’s Forward Deployed Engineering (FDE) org sits at the intersection of product, engineering, research, and go-to-market. We take frontier platform capabilities into the real world with design partners, turning raw customer signal into shipped software, repeatable patterns, and durable products.

The FDE Platform team is primarily a leverage function that scales the FDE org’s impact to OpenAI’s platform and products. We provide hands-on leverage by embedding with customer-tagged FDE pods to aid in architecting, product shaping, refactoring, and building. This team is perfect for highly collaborative software engineers who love innovating on cutting-edge products with other builders.

About the role

Platform Engineer is a role within Forward Deployed Engineering (FDE) for strong software and ML engineers who want to build new platform capabilities from scratch, grounded in real customer deployments.

You will partner with customer-tagged FDEs who are driving delivery and customer outcomes, and embed where you can provide the highest leverage. In practice that means working in the trenches on architecture, product shaping, refactoring, hardening, and reusable abstractions, while preserving the pod’s ownership of customer understanding and day-to-day execution. You will also collaborate closely with our B2B Platform Team and other long-term owners to align early on what should generalize, what should remain customer-specific, and what “ready for handoff” looks like.

This role does not require travel. It is based in San Francisco or New York. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel is optional-by-project and typically <%, with occasional spikes for key embeds or launches.

In this role you will

• Provide hands-on leverage to customer pods: embed with customer-tagged FDE teams to support generalization, contributing directly in architecture, product shaping, refactoring, and implementation.

• Turn repeated signals into platform bets: translate cross-customer patterns into crisp hypotheses with clear success criteria, scope, and a validation plan that fits real account constraints.

• Raise the engineering bar through tooling and mentorship: set org-wide quality norms through high-signal code review and pairing, and build lightweight developer tooling that makes good architecture, readability, and correctness the default across FDE.

• Collaborate as part of cross-functional platform teams: partner closely with B2B Product, customer-tagged FDEs, ops, and business partners to bring the right products and platform capabilities to market.

• Lead complex platform capabilities end-to-end when needed: for high-leverage primitives like our Context Platform, act as DRI from requirements through implementation, make key tradeoffs explicit, and pull in customer pods early to keep the work grounded in real deployments.

You might thrive in this role if you

• Bring 5+ years of software engineering or ML engineering experience with a track record of shipping 0→1 capabilities that other engineers or customers depend on. Experience in high-ambiguity, fast-iteration environments (startups or product-centric teams) is a plus.

• Have owned customer-adjacent technical work end-to-end, from scoping and hypothesis-setting through production adoption, and improved outcomes through structured iteration (instrumentation, evals, error analysis, and tightening success criteria over time).

• Have built or operated systems where reliability, security, and governance materially shaped design (permissions/RBAC, auditability, data access boundaries, rollout safety, observability, and incident-driven hardening).

• Communicate clearly across engineering, product, go-to-market, and customer-facing teams to drive alignment and shared understanding of customer needs and technical tradeoffs.

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About the Team

OpenAI’s mission is to ensure that artificial general intelligence (AGI) benefits all of humanity. A key part of achieving that mission is training models that deeply understand and reflect human preferences — the Human Data team is at the heart of that effort.

The Human Data engineering team creates the systems that enable scalable, high-quality human feedback. These systems are essential to how OpenAI trains and improves its most advanced models. Engineers on this team collaborate closely with world-class researchers to bring alignment techniques to life — from experimental ideas to production-ready feedback loops.

About the Role

We’re looking for software engineers to join the Human Data team and build the platforms, prototypes, tools, and infrastructure that power how our AI models are trained, aligned, and evaluated. You’ll partner with researchers and cross-functional teams to bring alignment ideas to life, influence future model training, and shape how models interact with the real world.

We’re looking for people who are excited by technical ownership, enjoy working across the stack, and are eager to solve ambiguous problems in a high-impact, fast-paced environment.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Build and maintain robust full-stack systems for feedback collection, data labeling, and evaluation pipelines, while maintaining high levels of security.

• Translate experimental alignment research into scalable production infrastructure, including inference and model training stacks.

• Design and iterate on user-facing tools and backend services to support high-quality data workflows

• Partner with researchers, engineers, and program leads to shape feedback loops and model interaction paradigms

• Drive infrastructure improvements that enable faster iteration and scaling across OpenAI’s frontier models, from internal research tooling all the way to production ChatGPT.

You might thrive in this role if you:

• Have strong software engineering fundamentals and experience building production systems at scale

• Enjoy full-stack development with end-to-end ownership — from backend pipelines to user interfaces

• Are motivated by high-impact collaboration with research teams and solving novel, ambiguous problems

• Are excited to shape how AI systems learn from human preferences and reflect a broad range of human values

• Care deeply about inclusive tooling and building systems that enhance model safety, reliability, and usefulness

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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Data Scientist, Preparedness

Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $347K – $400K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Preparedness team is an important part of the Safety Systems org at OpenAI, and is guided by OpenAI’s Preparedness Framework.

Frontier AI models have the potential to benefit all of humanity, but also pose increasingly severe risks. To ensure that AI promotes positive change, the Preparedness team helps us prepare for the development of increasingly capable frontier AI models. This team is tasked with identifying, tracking, and preparing for catastrophic risks related to frontier AI models.

The mission of the Preparedness team is to:

1. Closely monitor and predict the evolving capabilities of frontier AI systems, with an eye towards misuse risks whose impact could be catastrophic to our society

1. Ensure we have concrete procedures, infrastructure and partnerships to mitigate these risks and to safely handle the development of powerful AI systems

Preparedness tightly connects capability assessment, evaluations, and internal red teaming, and mitigations for frontier models, as well as overall coordination on AGI preparedness. This is fast paced, exciting work that has far reaching importance for the company and for society.

About the Role

We’re hiring a Data Scientist to help build, evaluate, and continuously improve mitigations that prevent extreme harms from AI systems. This role is for an experienced, highly autonomous individual contributor who can take ambiguous problem statements, structure rigorous analyses, and translate findings into actionable product and policy changes.

This position goes beyond “running evals.” You’ll help create mitigation intelligence and monitoring systems that enable OpenAI to detect issues early, measure effectiveness over time, and reduce both over-blocking (unnecessary friction) and under-blocking (missed harm).

What You’ll Do

• Evaluate and improve mitigation systems, including classifiers and detection pipelines across domains (e.g., biosecurity, cybersecurity, and emerging risk areas).

• Diagnose false positives and false negatives with deep error analysis, root cause investigation, and clear recommendations for mitigation adjustments.

• Build monitoring and measurement frameworks to track mitigation effectiveness over time and across user segments and use cases.

• Identify trends in over-blocking vs. under-blocking, quantify customer impact, and propose prioritized interventions.

• Develop insights from customer feedback, complaints, and usage patterns to detect shifts in adversarial behavior and system failure modes.

• Expand risk monitoring into new areas, including cybersecurity threats and model loss-of-control or sabotage scenarios, in partnership with domain experts.

• Communicate results to technical and executive stakeholders with crisp narratives, decision-ready metrics, and clear tradeoffs.

You might thrive in this role if you are:

• An autonomous operator: you can take a problem statement and independently structure the analysis end-to-end.

• Strong at executive-ready communication: concise, clear, and outcome-oriented.

• Skilled in turning analysis into productable changes: you’re comfortable influencing across functions to drive mitigation improvements.

Qualifications

• Significant experience in data science or applied analytics in high-stakes domains (e.g., security, trust & safety, abuse prevention, fraud, platform integrity, or reliability).

• Strong foundations in experimentation, causal thinking, and/or observational inference; ability to design robust measurement under imperfect data.

• Fluency in SQL and Python (or equivalent) for analysis, modeling, and building monitoring workflows.

• Experience building metrics, dashboards, and operational monitoring that meaningfully changes outcomes (not just reporting).

• Track record of driving cross-functional impact with engineering, product, and research partners

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Remote - US

Employment Type

Full time

Location Type

Remote

Department

Compensation

• Remote$157.6K – $222.4K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the team

The Codex Deployment Engineering team helps customers adopt OpenAI's coding tools throughout their software development lifecycle. We act as trusted technical partners, guiding engineering teams as they integrate Codex into their projects and workflows. Our customers span digital-native companies to global enterprises, and we work side-by-side to accelerate how they plan, build, and deliver software.

About the Role

We are seeking a technically deep, creativity-driven AI Deployment Engineer who is already a power user of AI coding tools and passionate about pushing the boundaries of developer productivity. You will partner directly with engineering leaders and hands-on builders to design, validate, and scale advanced AI workflows, often using Codex to prototype and build the very demos, integrations, and automations customers ultimately adopt.

This is a highly cross-functional role that blends technical architecture, product strategy, and customer-facing leadership. You’ll work closely with Sales, Solutions Engineering, Product, Applied Engineering, and the broader Codex organization to advocate for customer needs, shape product direction, and accelerate the successful deployment of intelligent coding systems across some of the world’s most influential companies.

In this role, you will:

• Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows.
• Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout.
• Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of your development process.
• Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely.
• Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex.
• Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams.
• Influence customer strategy and decision-making by framing how AI coding tools fit into their SDLC, technical roadmap, and organizational workflows.
• Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

You’ll thrive in this role if you:

• Have 5+ years of technical consulting, post-sales engineering, solutions architecture, or similar experience working directly with customers.
• Are an active power user of AI coding tools and have deeply customized your own developer workflow; you have a point of view on what makes engineers more productive.
• Enjoy building scrappy, high-signal demos, integrations, and prototypes that clearly articulate what Codex can enable, often using Codex to accelerate your own development process.
• Have experience delivering large, high-impact workshops or technical training to engineering teams and know how to craft sessions that are engaging, hands-on, and outcomes-driven.
• Have contributed technical guides, patterns, or examples publicly and care about clarity, pedagogy, and community impact.
• Communicate complex technical concepts in clear, persuasive written and verbal form especially when helping customers make strategic decisions about where and how to apply AI.
• Are excited by ambiguous, rapidly evolving problem spaces and enjoy iterating toward novel solutions hand-in-hand with customers.
• Care about customer success, reliability, safety, and operational excellence as much as a great product.

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The Codex Deployment Engineering team helps customers adopt OpenAI's coding tools throughout their software development lifecycle. We act as trusted technical partners, guiding engineering teams as they integrate Codex into their projects and workflows. Our customers span digital-native companies to global enterprises, and we work side-by-side to accelerate how they plan, build, and deliver software.

About the Role

We are seeking a technically deep, creativity-driven AI Deployment Engineer who is already a power user of AI coding tools and passionate about pushing the boundaries of developer productivity. You will partner directly with engineering leaders and hands-on builders to design, validate, and scale advanced AI workflows, often using Codex to prototype and build the very demos, integrations, and automations customers ultimately adopt.

This is a highly cross-functional role that blends technical architecture, product strategy, and customer-facing leadership. You’ll work closely with Sales, Solutions Engineering, Product, Applied Engineering, and the broader Codex organization to advocate for customer needs, shape product direction, and accelerate the successful deployment of intelligent coding systems across some of the world’s most influential companies.

In this role, you will:

• Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows.

• Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout.

• Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of your development process.

• Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely.

• Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex.

• Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams.

• Influence customer strategy and decision-making by framing how AI coding tools fit into their SDLC, technical roadmap, and organizational workflows.

• Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

You’ll thrive in this role if you:

• Have 5+ years of technical consulting, post-sales engineering, solutions architecture, or similar experience working directly with customers.

• Are an active power user of AI coding tools and have deeply customized your own developer workflow; you have a point of view on what makes engineers more productive.

• Enjoy building scrappy, high-signal demos, integrations, and prototypes that clearly articulate what Codex can enable, often using Codex to accelerate your own development process.

• Have experience delivering large, high-impact workshops or technical training to engineering teams and know how to craft sessions that are engaging, hands-on, and outcomes-driven.

• Have contributed technical guides, patterns, or examples publicly and care about clarity, pedagogy, and community impact.

• Communicate complex technical concepts in clear, persuasive written and verbal form especially when helping customers make strategic decisions about where and how to apply AI.

• Are excited by ambiguous, rapidly evolving problem spaces and enjoy iterating toward novel solutions hand-in-hand with customers.

• Care about customer success, reliability, safety, and operational excellence as much as you care about technical ingenuity.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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Seattle; San Francisco

Employment Type

Full time

Department

Applied AI

Compensation

• $325K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Monetization team is a new cross-functional group working across engineering, product, research, and design to build the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to develop user-first, privacy-preserving monetization products, including next-generation ads experiences that strengthen user trust, unlock economic opportunity, and support OpenAI’s long-term innovation.

Monetization plays a critical role in enabling OpenAI to continue pushing the boundaries of AI capabilities while ensuring the benefits of AGI are broadly shared. We believe monetization must be aligned with user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem of developers and businesses.

This team operates in a greenfield environment and moves quickly through prototyping, experimentation, and iterative deployment. We partner closely with Product, Design, and Research to bring research breakthroughs into real-world systems at global scale.

About the Role

We’re looking for a Senior iOS Engineer to build the core mobile monetization experiences and iOS foundations that power how users sign up, manage their account, access family features, pay for services, and interact with monetized experiences safely and confidently across OpenAI’s mobile apps.

This is a 0→1, greenfield opportunity: you’ll help define the architecture, primitives, and reusable frameworks that product teams depend on to ship quickly while meeting the highest standards for security, reliability, privacy, and user trust. You’ll partner closely with backend, product, design, safety, and research to translate evolving requirements into high-quality iOS systems and experiences.

In this role, you will:

• Build and ship new monetization experiences on iOS, including purchase flows, subscription/plan management, and monetization-adjacent surfaces that showcase the power of AI.
• Design and maintain shared iOS frameworks and primitives for account, trust, and commerce flows used across OpenAI’s mobile apps (e.g., identity, entitlements, payments, policy-aware UX).
• Own complex client-side systems spanning UI, networking, local state, observability, and Apple platform integrations (StoreKit, Sign in with Apple, device security primitives, etc.).
• Optimize performance, reliability, and responsiveness for global-scale usage, including robust offline/poor-network handling and safe rollout strategies.
• Establish and evolve testing frameworks (unit, integration, snapshot/UI, and end-to-end) and refine architecture for long-term maintainability.
• Collaborate deeply with Product, Design, Research, and backend teams to translate ambiguous goals into durable systems and delightful experiences.
• Help uphold the highest levels of safety, privacy, fairness, and policy compliance across monetization experiences.
• Provide technical leadership through architectural reviews, mentoring, and setting engineering standards across iOS monetization foundations.

You might thrive in this role if you

• Have 4+ years of professional software engineering experience with significant iOS ownership.
• Have a proven track record shipping high-quality iOS apps in production for business-critical, reliability-sensitive systems.
• Are fluent in Swift and experienced with Apple’s ecosystem (Xcode, SwiftUI).
• Have owned complex mobile systems end-to-end (architecture, delivery, instrumentation, quality, and operations).
• Care deeply about performance, security, privacy, and user experience, especially in flows involving identity and payments.
• Thrive in 0→1 environments where systems, architecture, and user experiences must be defined from scratch.

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We’re hiring software engineers to make the Workload team more productive. The Workload team maintains the core components of OpenAI’s training and inference frameworks and helps execute frontier experiments.

About the Role

We’re looking for someone who cares about the developer experience of working in and around OpenAI’s core training and inference frameworks.

In this role you will:

• Be responsible for optimizing the development workflows of the engineers around you

• Work within various Workload teams to address their specific needs, but collaborate with the centralized teams that own various aspects of development experience

• Optimize iteration speed, both broadly, and in particular by optimizing specific teams’ CI

• Improve reliability, for instance, by driving testing strategy for particular components

• Work through the long tail of things that it takes to build libraries and systems that will delight researchers

You might thrive in this role if:

• You are motivated by helping people. You believe a thing that separates great teams from good teams are the players willing to do whatever work it takes, without ego.

• You believe in the power of developer experience. Something magical happens when people can quickly and confidently iterate on a simple codebase, but this magic is fragile and must be fought for.

• When you see someone trip over something, no matter how small, your first instinct is asking yourself what it would take for that to not happen again. Your second instinct is clicking merge on the PR you’ve already written to make it so.

• You are pragmatic. You have the ability to see the world through a perfectionist’s eyes, but are not yourself a perfectionist. You know which problems to pick and when to switch to making progress on a different problem.

• You like going end-to-end on things. You love co-design — that feeling when you were only able to find the right solution because you both deeply understand the users that interact with a system and the system itself.

• You like working with Python

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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About the Team

With Codex we’re building an AI software engineer. One that you can pair with, delegate to, or even ask to take on future tasks proactively. Our team is a fast-moving group within OpenAI, bringing together research, engineering, design, and product. We iteratively build the Codex agent harness and product to get the most out of the model, and we iteratively train the model to be great at complex software engineering tasks.

About the Role

This role is for a software engineer working on Codex, with a specific focus on enabling team-scale adoption across a wide spectrum of environments, from internal teams at OpenAI to external customers ranging from startups to large enterprises. You’ll work directly with customers, Go To Market (GTM) teams, and other engineers and researchers across Codex and OpenAI. You will turn diverse team requirements into products that scale across organizations. The role bridges what teams with Codex’s capabilities, ensuring that they are robust, repeatable, and deeply aligned with how developers work in real-world environments. You’ll own systems end-to-end (architecture, implementation, production operations) with a strong bias for both quality and velocity.

In this role, you will:

• Ship fundamental capabilities including analytics dashboards and APIs, compliance and audit surfaces, workspace RBAC and admin controls, managed configuration and constraints, and rate limits, usage, and pricing primitives for teams.

• Design and build robust, full-stack services and APIs that power Codex across surfaces (web/app, CLI/local, IDEs, CI/CD) with strong observability, reliability, and security.

• Enable standardized team deployments by building team configuration packaging and distribution patterns that make it easy to roll out consistent experiences across workspaces.

• Integrate with enterprise identity and governance systems (e.g., SSO/SAML/OIDC, SCIM, RBAC, policy enforcement), and build data-access patterns that are secure, performant, and compliant for large customers.

• Partner with GTM to work hands-on with teams through deep engagements to accelerate adoption, iterate rapidly, and translate real-world feedback into scalable product and platform improvements.

You might thrive in this role if you:

• Have strong software engineering fundamentals and experience turning ideas into productionized systems, thinking holistically about speed, performance, and user experience.

• Are proficient in one or more backend languages (e.g., Python, Go, Rust) and distributed systems concepts, with a focus on reliability, observability, and security.

• Enjoy building cross-cutting platform capabilities that unlock product velocity, and you’re comfortable working across services, APIs, and end-user product surfaces.

• Have experience with team/enterprise foundations such as identity and access (SAML/OIDC), SCIM, RBAC, audit/compliance logging, policy enforcement, and data governance controls.

• Have built developer tools and workflows (CLI/IDE/SDK), automation systems (triggers/scheduling), or integration platforms that connect products to a broader ecosystem of tools.

• Like working directly with users/customers (or alongside GTM/solutions teams), and can translate messy, diverse requirements into opinionated implementations that scale across many teams.

• Enjoy 0 -> 1 environments, can navigate ambiguity, and bring crisp product thinking to technical trade-offs.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

Location

San Francisco

Employment Type

Full time

Department

Applied AI

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Monetization team is a new cross-functional group working across engineering, product, research, and design to build the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to develop user-first, privacy-preserving monetization products—including next-generation ads experiences—that strengthen user trust, unlock economic opportunity, and support OpenAI’s long-term innovation.

Monetization plays a critical role in enabling OpenAI to continue pushing the boundaries of AI capabilities while ensuring the benefits of AGI are broadly shared. We believe monetization must be aligned with user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem of developers and businesses.

This team operates in a greenfield environment and moves quickly through prototyping, experimentation, and iterative deployment. We partner closely with Product, Design, and Research to bring research breakthroughs into real-world systems at global scale.

About the Role

We’re looking for an experienced Software Engineer to help build the core monetization and ads systems at OpenAI. This is a foundational role responsible for designing and implementing the infrastructure, APIs, and user-facing experiences that will power OpenAI’s next-generation monetization products—including ads.

You’ll work across the full technical stack to architect, build, and ship 0→1 systems that are robust, safe, and scalable. You will collaborate deeply with Product, Design, and Research to define the future of monetized AI experiences and ensure these systems meet OpenAI’s highest standards for safety, privacy, and policy alignment.

This role is exclusively based across our San Francisco and Seattle sites. We offer relocation assistance to new employees.

In this role, you will:

• Design, build, and scale the core infrastructure behind OpenAI’s monetization and ads products

• Develop advertiser-facing APIs and tools that enable the creation, management, and measurement of ads

• Build integrations with external partners and help create enterprise-grade monetization products

• Architect distributed systems and services that operate at massive scale with strong reliability, usability, privacy, and performance guarantees

• Work closely with Product, Research, and Design to translate product requirements into high-quality technical solutions

• Drive 0→1 product development through rapid prototyping, experimentation, and iterative deployment

• Contribute to technical strategy, propose new directions, and help shape the roadmap for monetization and ads platforms

• Maintain high engineering rigor through strong testing, documentation, and operational best practices

• Uphold the highest levels of safety, privacy, fairness, and policy compliance across all monetization systems

• Help continuously refine engineering processes, culture, and best practices within a fast-moving, multidisciplinary team

You might thrive in this role if you:

• Have 7+ years of experience building high-scale consumer, enterprise, or ads-related systems

• Have a track record of architecting and delivering complex distributed systems with high reliability and performance requirements

• Are comfortable navigating ambiguous product spaces and contributing to technical roadmaps and long-term direction

• Bring strong product intuition and enjoy collaborating deeply with Product, Design, and Research

• Thrive in 0→1 environments where systems, architecture, and user experiences must be defined from scratch

• Communicate clearly, think holistically, and make decisions rooted in both user needs and long-term sustainability

• Are motivated by building privacy-preserving, safe, and user-first monetization systems

• Bonus: Have experience in ads systems, marketing, or related fields

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Engineering Manager, Identity Infrastructure

Location

San Francisco

Employment Type

Full time

Department

Applied AI

Compensation

• $293K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

Identity is the foundation of trust in an AI-powered world. As people build a rich personal and organisational context in ChatGPT, they need systems that protect it, remember it, and let them share it securely across apps and agents. Without strong identity, context stays locked away or exposed. With it, AI becomes safe, personal, and truly useful, empowering users while keeping control firmly in their hands. Our team builds the trusted OpenAI account layer that connects people, enterprises, developers, and agents to OpenAI apps and the broader AI ecosystem. Our systems are the front door to OpenAI—held to an exceptionally high bar for availability, security, and latency—and are depended on by many internal teams.

About the Role

As an Engineering Manager, Identity Infrastructure, you will lead a team owning foundational identity services that must be highly reliable, secure, and fast under extreme load. You’ll set direction, grow and support the team, and partner closely across engineering and security to strengthen the platform that enables trusted access to OpenAI products.

We’re looking for people who combine strong infrastructure depth with people leadership—someone who can guide technical strategy, run reliable operations, and build a high-performing team delivering mission-critical systems.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

What You’ll Do:

• Build, lead, and develop a high-performing engineering team. Coach and mentor engineers and senior technical leads to increase their impact and support their growth.

• Define and own the technical strategy, roadmap, and execution for OpenAI’s core identity systems.

• Guide the architecture of distributed systems operating at massive scale, with high requirements for reliability, privacy, and performance.

• Partner closely with Product and Infrastructure teams to support new product needs and uphold high system standards.

• Ensure the team operates effectively, maintains strong engineering rigor, and consistently delivers against ambitious goals.

• Establish processes and a culture that enable fast learning, high trust, and responsible innovation.

• Lead the team responsible for the authentication stack and token exchange infrastructure, ensuring high availability, strong security posture, and low-latency performance.

• Own and improve request validation at the edge for logged-in users, delivering extremely high reliability and fast validation under high throughput.

• Oversee systems that enable secure access to user data, improving scalability, reliability, and access patterns for internal consumers across the company.

You might thrive in this role if you:

• Have 3+ years of experience managing high-performance engineering teams in fast-paced, rapidly shifting environments.

• Have 10+ years of experience building, scaling, and operating distributed systems at large scale.

• Care deeply about people leadership, coaching, and helping engineers achieve peak performance.

• Communicate clearly, think holistically, and make decisions grounded in both user needs and long-term sustainability.

• Have experience managing an infrastructure team and are effective at hiring, coaching, and developing engineers while maintaining strong operational excellence.

• Have deep experience building and operating high-scale backend systems, with a track record of improving reliability, latency, and security in production.

• Bring broad infrastructure expertise across the stack—from hardware and cluster management to services and storage layers.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of artificial general intelligence and work to align its development with human values. Our mission is to create a world where AI is a powerful tool for humanity, and we believe that this requires a multidisciplinary approach to research and development. We are committed to transparency and open communication, and we believe that sharing our knowledge and insights with the world is essential to advancing the field of AI. We are a team of researchers, engineers, and designers who are passionate about creating a future where AI is a force for good. We are based in San Francisco, California, and we are proud to be a part of the vibrant tech community in the Bay Area. We are committed to diversity, equity, and inclusion, and we believe that a diverse and inclusive team is essential to creating a better future for all. We are always looking for talented individuals who share our vision and values, and we encourage you to join our team and help us shape the future of AI.

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San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Consumer Products

Compensation

• $293K – $325K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Connectivity Software Engineering team is responsible for enabling seamless, secure, and high-performance wireless connectivity across OpenAI’s products. We design and optimize Bluetooth, BLE, Wi-Fi, and emerging wireless technologies to ensure robust device pairing, network performance, and interoperability. Our work spans kernel drivers, system services, and user-level tools, with a focus on real-world performance, scalability, and reliability.

About the Role

OpenAI is seeking a Connectivity Software Engineer to design, implement, and optimize wireless connectivity features across our product ecosystem. You’ll work at the intersection of systems software, wireless standards, and hardware integration—building robust pairing and provisioning flows, debugging low-level protocols, and driving performance under real-world RF constraints. You will also support certification, field interoperability, and fleet-scale connectivity infrastructure.

This role is based in San Francisco, CA. We use a hybrid work model of 4 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Design, implement, and debug Bluetooth/BLE and Wi-Fi features across kernel drivers, BlueZ/wpa\_supplicant/hostapd, and systemd/D-Bus services

• Deliver robust pairing, bonding, and provisioning flows (GATT/GAP, LE Audio/LC3, WPA3/802.1X, captive portals, NAN)

• Optimize link performance: throughput, latency, jitter, roaming, coexistence (BT↔Wi-Fi), and power modes (TWT, WoWLAN)

• Build reliable network management using NetworkManager/nmcli, nl80211/cfg80211/mac80211, DNS/DHCP/mDNS, P2P/SoftAP

• Instrument and analyze with packet captures and tooling (btmon/hcidump, Wireshark, iperf, eBPF/perf, spectrum sniffers)

• Drive interoperability and certification readiness (Bluetooth SIG, Wi-Fi Alliance) and resolve field issues with root-cause fixes

• Contribute to OTA-safe configuration, telemetry, and diagnostics for fleet-scale operation

You might thrive in this role if you:

• Have deep experience shipping wireless features on Linux-based products

• Are highly proficient in C/C++ with scripting experience (Python or shell) and systems debugging (gdb, strace, logs, packet traces)

• Possess deep knowledge of Bluetooth Classic/BLE (HCI, L2CAP, GATT/GAP, profiles) and Wi-Fi (802.11 a/b/g/n/ac/ax, WPA2/3, nl80211, NAN)

• Bring hands-on experience with BlueZ, wpa\_supplicant/hostapd, NetworkManager, and driver bring-up on ARM64 or x86 platforms

• Have a proven track record of improving real-world performance and reliability under RF constraints

Preferred qualifications:

• Experience with LE Audio (LC3), BLE Mesh, advanced roaming (802.11k/v/r), QoS/WMM, multicast/IGMP

• Coexistence tuning across radios (BT/Wi-Fi/UWB/mmWave) and antenna/RF fundamentals with test equipment workflows

• Familiarity with UWB (IEEE 802.15.4z, FiRa) ranging/integration; mmWave/Wi-Gig (802.11ad/ay)

• Experience with security and provisioning at scale (EAP-TLS, device identity, secure boot, disk/network hardening)

• Background in building factory test, interoperability, and certification test plans; upstream/open-source contributions

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

Location

San Francisco

Employment Type

Full time

Department

Applied AI

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Monetization team is a new cross-functional group working across engineering, product, research, and design to build the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to develop user-first, privacy-preserving monetization products—including next-generation ads experiences—that strengthen user trust, unlock economic opportunity, and support OpenAI’s long-term innovation.

Monetization plays a critical role in enabling OpenAI to continue pushing the boundaries of AI capabilities while ensuring the benefits of AGI are broadly shared. We believe monetization must be aligned with user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem of developers and businesses.

This team operates in a greenfield environment and moves quickly through prototyping, experimentation, and iterative deployment. We partner closely with Product, Design, and Research to bring research breakthroughs into real-world systems at global scale.

About the Role

We’re looking for an experienced Software Engineer to help build the core infrastructure behind OpenAI’s monetization and ads systems. In this foundational role, you’ll architect and implement distributed systems that power OpenAI’s monetization stack—focusing on reliability, performance, privacy, and large-scale operation.

You’ll work across backend, systems, and platform layers to define and implement 0→1 infrastructure, partnering closely with Product, Design, and Research to shape the future of monetized AI experiences. Your work will enable both internal and external teams to build on safe, scalable, and robust monetization primitives.

This role is exclusively based across our San Francisco & Seattles sites. We offer relocation assistance to new employees.

In this role, you will:

• Design and build the foundational backend and infrastructure powering OpenAI’s monetization and ads systems

• Architect large-scale distributed systems that meet strict requirements for reliability, privacy, security, and performance

• Develop APIs, infrastructure services, and internal platforms that support ads creation, delivery, measurement, and optimization

• Work closely with Product, Research, and Design to translate requirements into scalable technical solutions

• Drive 0→1 infra development through rapid prototyping, experimentation, and iterative improvements

• Contribute to the long-term technical strategy and roadmap for the monetization infra stack

• Ensure high engineering rigor through excellent testing, documentation, observability, and operational best practices

• Build for safety, privacy, fairness, and policy alignment from first principles

• Collaborate across engineering orgs to ensure the infra layer is flexible, performant, and resilient

You might thrive in this role if you:

• Have 10+ years of experience building and operating large-scale distributed systems

• Have experience designing mission-critical systems with demanding reliability, performance, and correctness requirements

• Think in systems—architecture, data flows, operational concerns, observability, and long-term maintainability

• Are comfortable defining technical direction in ambiguous 0→1 environments

• Enjoy working cross-functionally to shape product requirements and system capabilities

• Communicate clearly, reason holistically, and make decisions grounded in user needs and long-term system health

• Bonus: Experience in ads systems, marketplaces, AI/ML infra, or other monetization-intensive domains

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through research and collaboration.

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