You will gain insight into modern development processes for automotive control units and acquire hands-on experience in analysing, evaluating, and further developing hardware architectures, supporting tests of electronic prototypes, circuit and schematic design as well as layout of microcontrollers, power supplies, output stages, and CAN/Ethernet communication interfaces, using AI-based tools to automate review processes, and programming microcontrollers.

As a Working Student, you will be an enrolled student in Electrical Engineering, Mechatronics, or a comparable field of study, with a strong interest in hardware development and enthusiasm for e-mobility, solid knowledge of fundamental controls electronics and related components, team spirit, strong interpersonal skills, and intercultural openness, and good German and English language skills.

With mutual interest, there is the option to extend your collaboration through a thesis project.

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Responsibilities:

• Build the core application and workflow agents for Market Data Operations in Python; integrate with AWS and internal systems like the Market Data Warehouse.
• Ingest and understand contracts at scale, using LLMs to extract costs, fee schedules, entitlements, renewal terms, and payment details.
• Connect the dots between contracts, entitlements, invoices, and payments so Ops, Legal, and Finance can see a single 'source of truth' and catch issues early.
• Design and tune LLM workflows (prompt engineering, tool/MCP integration, structured outputs) for contract Q&A, summarization, and exception flagging.
• Own monitoring and controls for the AI system: logging, metrics, guardrails, and human-in-the-loop review to keep performance, reliability, and quality high.
• Work directly with stakeholders (Market Data Ops, analysts, Legal, Finance/AP) to understand their workflows and quickly iterate on features that actually get used.

Required Skills & Experience:

• Bachelor's degree in Computer Science or a related field.
• 5+ years of professional experience with Python, including building production services (Django, Flask, or FastAPI).
• Experience working with unstructured documents (contracts, PDFs, legal docs) and turning them into structured data.
• Prompt engineering and working with structured JSON outputs
• Comfort wiring models into real applications (tool/MCP-style integrations, APIs).
• Experience using cloud platform, ideally AWS.
• Able to define and track quantitative metrics for AI features (accuracy, latency, cost, etc.).
• Strong communication skills and comfortable working directly with non-technical users.
• Enjoys a start-up-like environment inside a large firm: small team, high ownership, fast iteration.

Nice to Have:

• Experience building AI solutions in financial services, especially around market data, vendor management, or legal/contract workflows.
• Familiarity with entitlements/governance and large internal data platforms (e.g., a Market Data Warehouse).

The estimated base salary range for this position is $175,000 to $250,000, which is specific to New York and may change in the future. Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package.

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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 400,000 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

As the Principal Engineer for the Contractors team, you will play a pivotal role in shaping the future of Gusto’s flagship Payroll product,one of the core pillars of our platform. You will design, build, and scale the capabilities that power essential experiences for our customers. Working collaboratively with product managers, designers, and other engineers, you will deliver impactful features that meet customer needs and elevate user experiences.

As a Gusto Engineer at this level, you’ll guide projects end-to-end,shaping initial feature specifications, driving architectural decisions to bring systems closer to their desired end states, executing on complex initiatives, and maintaining code that powers mission-critical functionality. Beyond technical contributions, you’ll help define and contribute to the broader strategy of how Gusto continues to build and scale its Payroll product.

If you’re excited about solving complex, high-impact problems and want to contribute to a product that touches the lives of millions, we’d love to have you on board!

About the Team

Payroll serves as Gusto's core product, used by each of our 500,000+ customers and contributing significantly to our annual recurring revenue of over $800,000,000. Although we hold the leading market position for SMBs in the US, the market remains highly fragmented, with an estimated 90% still in need of a superior solution. The Contractors team empowers businesses to onboard and pay contractors in 120+ countries with ease and speed. This includes critical functionalities such as payroll setup, preparation, and submission, historical reporting, time tracking, and shift scheduling.

As a key member of this team, you’ll have the opportunity to make a profound impact on both the product and the customers who depend on it daily.

Here’s what you’ll do day-to-day:

• Architect, build, and maintain scalable, secure, and resilient backend systems to support Gusto’s Payroll products.

• Function as a Technical Lead across multiple teams in Pay Group, helping us keep engineers unblocked and deliver high-quality work supporting our long-term goals.

• Help scale one of the largest Ruby/Rails and TypeScript/React applications in the world.

• Collaborate on complex and ambiguous problems with partnerships from Engineering, Product Management, Design, Data Science, Compliance, Operations, and other cross-functional teams.

• Mentor and grow fellow engineers working to create holistic and scalable solutions.

• Drive the product development process from concept to launch, delivering delightful products that make payroll, taxes, and compliance simple and easy.

• Engage in a highly supportive environment working with others to drive productivity and innovation.

Here’s what we're looking for:

• 15+ years of professional software development experience.

• Experience as a tech lead, overseeing and successfully delivering projects that span multiple teams.

• Enthusiasm for a collaborative, test-driven development environment.

• Proven experience building and maintaining resilient backend systems that support customer-facing products, including optimizing existing systems for performance, reliability, and scalability.

• Ability to produce maintainable, structured, and well-documented code.

• Expertise in developing and maintaining RESTful APIs, GraphQL endpoints, and backend services, ensuring seamless integration with frontend systems and third-party services.

• Demonstrated ability in scaling engineering organizations, with a strong focus on individual and team development and mentorship.

• Experience in highly cross-functional environments working on highly complex products.

• Ability to clearly communicate technical complexity and facilitate informed trade-offs among stakeholders.

• Experience using AI tools to build, test, and iterate on products quickly.

• Understanding of how to evaluate AI-driven outputs using clear success criteria.

• A commitment to staying current on emerging backend technologies and AI frameworks and patterns, regularly experimenting with new approaches.

• Willingness to contribute to shared tools or templates that enhance the speed and safety of AI experimentation.

Our cash compensation amount for this role is targeted at $251,000-$309,000 /yr for New York. Final offer amounts are determined by multiple factors, including candidate experience and expertise, and may vary from the amounts listed above.

Gusto has physical office spaces in Denver, San Francisco, and New York City. Employees who are based in those locations will be expected to work from the office on designated days approximately 2-3 days per week (or more depending on role). The same office expectations apply to all Symmetry roles, Gusto's subsidiary, whose physical office is in Scottsdale. Note: The San Francisco office expectations encompass both the San Francisco and San Jose metro areas. When approved to work from a location other than a Gusto office, a secure, reliable, and consistent internet connection is required. This includes non-office days for hybrid employees.

Our customers come from all walks of life and so do we. We hire great people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger. If you share our values and our enthusiasm for small businesses, you will find a home at Gusto.

Gusto is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic.

Gusto considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Gusto is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures.

We want to see our candidates perform to the best of their ability. If you require a medical or religious accommodation at any time throughout your candidate journey, please fill out this form and a member of our team will get in touch with you.

Gusto takes security and protection of your personal information very seriously. Please review our Fraudulent Activity Disclaimer. Personal information collected and processed as part of your Gusto application will be subject to Gusto's Applicant Privacy Notice.

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Key responsibilities include:

• Working with a cross-functional team to design market-leading hardware products.
• Full cycle PCB design, including collecting requirements, schematic design, component selection, supervision or completion of layout, bring-up, test, debug, and integration with the system.
• Driving product maturity through rapid prototyping, design verification, and qualification activities.
• Applying modern design standards and guidelines to create high-reliability, highly manufacturable assemblies.
• Implementing combinations of high-speed digital, precision analog, RF, and high-power designs.
• Leading the team in establishing internal guidelines for design for manufacturing (DFM) and quality fabrication and assembly outputs.
• Conducting peer-level and cross-discipline design reviews.
• Low-level firmware development for bring-up and test.
• Root cause analysis in support of production operations.

Requirements include:

• 4+ years of professional experience in hardware product development.
• Full understanding of PCB Layout techniques to meet mechanical, electrical, and manufacturing requirements.
• Ability to read and interpret schematics and apply best practices appropriate for each design.
• Familiarity with standard interfaces such as Ethernet, CAN, I2C, SPI, PCIe, USB, etc.
• Familiarity with common MCU, CPU, FPGA devices and technologies.
• Knowledge of modern analog and digital electronics and electronic circuits.
• Ability to determine work priorities based on broad direction from management.
• Experience using Altium Designer or equivalent E-CAD tools.
• Excellent communication skills and ability to work effectively with others.
• Ability to work within our company and team culture.
• Must have an active U.S. Secret security clearance.

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Developer Productivity & Tooling

• Drive cross-functional programs to improve developer environments, CI/CD infrastructure, and release processes that enable rapid innovation while maintaining high security standards

• Coordinate large-scale migrations and platform modernization efforts across engineering teams

• Partner with teams to measure and improve developer productivity metrics, identifying bottlenecks and driving systematic improvements

• Lead initiatives to integrate AI tools into development workflows, helping Anthropic be at the forefront of AI-assisted research and engineering

Infrastructure Reliability & Operations

• Drive programs to establish and achieve reliability targets across training infrastructure and production services

• Coordinate incident response improvements, post-mortem processes, and on-call rotations that help teams operate effectively

• Establish metrics and dashboards to track infrastructure health, capacity utilisation, and operational excellence

Cross-functional Coordination

• Serve as the critical bridge between infrastructure teams, research, and product, translating technical complexities into clear updates for a variety of audiences

• Consult with stakeholders to deeply understand infrastructure, data, and compute needs, identifying solutions to support frontier research and product development

• Drive alignment on priorities and timelines across teams with competing constraints

You'll be a good fit if you have 5+ years of technical program management experience, with a track record of successfully delivering complex infrastructure programs in ML/AI systems or large-scale distributed systems. You'll also need a deep technical understanding of infrastructure systems, strong stakeholder management skills, and the ability to navigate competing priorities-confirming data-driven technical decisions.

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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 400,000 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 is scaling our AI-powered risk function to support a complex, multi-entity business operating in highly regulated environments. As the Enterprise Risk Management Lead, you will own and operate Gusto's Enterprise Risk and Third Party Risk Management programs , built AI-first, designed to scale, and built to enable the business to move fast without breaking things.

This is a People Empowerer (manager) role. You balance hands-on program leadership with managing and developing a team of compliance professionals. You navigate the tension between "doing the work" and "leading the work" , contributing directly to complex, high-impact programs while ensuring your team delivers with excellence.

You are a change agent who influences how automated risk management gets done at Gusto, models AI-enabled ways of working, and helps others grow their own capabilities in the process.

You will champion the adoption of AI, machine learning, and process automation across risk monitoring, control testing, incident management, and reporting , and you will partner with Product, Data Science, and Engineering to make it explainable, adopted, compliant, and scalable.

Here’s what you’ll do day-to-day:

You manage initiatives that are complex in both scope and impact, influencing the strategic direction of Gusto's compliance risk management framework.

You apply a deep understanding of the regulatory landscape and how it intersects with Gusto's business model to proactively design and lead cross-functional risk programs.

You translate complex risk topics into clear, actionable guidance that senior leaders can immediately understand and operationalize.

You lead cross-functional working groups, align divergent perspectives, and drive cohesive progress toward shared goals , with minimal oversight.

As a PE, you balance individual risk and compliance contribution with team leadership.

You manage operations, professional development, resource allocation, and performance , while staying close enough to the work to be a credible, hands-on partner to your team and stakeholders.

You model responsible AI use, and act as a source of knowledge and mentorship , supporting your team's AI journey and helping others apply it responsibly and effectively.

AI-Enabled Risk Operations, Innovation & Transformation

This is how you and your team operate , not a side project.

• Champion the adoption of AI, machine learning, process automation, and advanced analytics to improve risk monitoring, control testing, and reporting across ERM, TPRM, and broader compliance functions

• Lead the integration of AI and automation into every phase of the risk lifecycle: vendor assessments, document ingestion and analysis, continuous monitoring and alerting, risk scoring, prioritization, and trend analysis

• Build intelligent risk monitoring and evaluation systems , including auto-tagging for risk issues, audit requests, and regulatory changes , that improve real-time visibility and eliminate manual effort across the enterprise risk portfolio

• Drive the digitalization of risk tools including RCSAs, KRIs, incident reporting, and audit tracking , transforming periodic, reactive processes into continuous intelligence systems with live leading and lagging indicators that enable real-time decision-making

• Partner with Product, Data Science, and Engineering to define requirements for AI-driven workflows, decisioning engines, and dashboards , ensuring explainability, auditability, and regulatory defensibility of all AI-enabled risk decisions

• Design and build intelligent dashboards and reporting tools that deliver real-time risk visibility and decision-quality insights to senior leadership and cross-functional stakeholders

• Design AI workflows with appropriate validation loops, human-in-the-loop checkpoints, and guardrails , ensuring outputs are reliable, governable, and meet regulatory standards before being used to frame risks, recommendations, or decisions

• Stay current on AI advancements and emerging technologies and proactively integrate new capabilities into team operations to increase velocity and scale

• Model responsible AI use , supporting ICs in their AI journeys and fostering a culture of intentional experimentation, accountability, and continuous improvement

Enterprise Risk Management

• Design, implement, and continuously improve Gusto's ERM framework, ensuring alignment with best practices and Gusto's stage of growth and strategic priorities across all entities

• Define and maintain Gusto's enterprise risk taxonomy, risk appetite statement, and key risk indicators spanning operational, regulatory, technology, financial, and reputational risk domains

• Lead Gusto's Enterprise Risk Management process , driving integration of risk practices across business functions, promoting a proactive risk culture, and ensuring incident management, root cause analysis, and lessons learned are systematically captured in an automated, AI forward way.

• Apply AI-assisted insights to enterprise risk datasets to surface systemic patterns, validate assumptions, prioritize risks, and deliver proactive, data-driven advisory to senior leadership

• Monitor the regulatory landscape (OCC, FDIC, CFPB, SEC, FINRA, GDPR, NIST, ISO, SOC) and leverage AI to proactively incorporate changes before they become compliance gaps

• Act as a key advisor to senior compliance leadership , translating complex risk findings into clear, actionable recommendations with minimal oversight

Third Party Risk Management (TPRM)

• Design, implement, and independently manage a high-impact, AI-first TPRM program with clear milestones, progress tracking, and measurable outcomes across all Gusto entities

• Manage the full third-party risk lifecycle , onboarding and risk profiling, periodic assessments, issue management, corrective action tracking, and offboarding , across suppliers, product partners, contractors, service providers, and cloud service providers , and do so in an AI and automated way.

• Maintain a centralized, authoritative vendor risk inventory and risk register, ensuring real-time visibility into Gusto's third-party risk posture

• Conduct periodic AI-driven audits and reviews of third-party compliance with contractual obligations and regulatory standards, identifying patterns that inform continuous program improvement

• Serve as the central orchestrator across Compliance, Security, Legal, Procurement, IT, and GRC for proactive and reactive third-party incident management

• Own Gusto's TPRM policy and maintain comprehensive documentation , risk assessments, audit findings, corrective actions , ensuring full accountability and traceability

People Leadership & Team Development

• Balance individual compliance contribution with team leadership , managing operations, professional development, resource allocation, and performance while staying close to the work

• Coach and develop ICs toward next

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In this role, you will push the boundaries of container orchestration far beyond existing systems like Kubernetes, manage exascale compute resources, optimize for high-performance training runs and production serving, and collaborate closely with research and systems teams to deliver reliable, ultra-scalable infrastructure that enables xAI's next-generation models and applications.

Responsibilities include building and managing massive-scale clusters, designing, developing, and extending an in-house container orchestration platform, collaborating with research teams to architect and optimize compute clusters, profiling, debugging, and resolving complex system-level performance bottlenecks, and owning end-to-end infrastructure initiatives.

To succeed in this role, you will need deep expertise in virtualization technologies and advanced containerization/sandboxing, strong proficiency in systems programming languages such as C/C++ and Rust, and proven track record profiling, debugging, and optimizing complex system-level performance issues.

Preferred skills and experience include experience in Linux kernel development, hypervisor extensions, or low-level system programming for compute-intensive workloads, operating or designing large-scale AI training/inference clusters, and familiarity with performance tools, tracing, and debugging in production distributed environments.

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Strong candidates will have a track record of solving large-scale systems problems and low-level optimization. They should have significant experience optimizing ML systems for TPUs, GPUs, or other accelerators, and be results-oriented with a bias towards flexibility and impact.

Responsibilities:

• Identify and address performance issues across multiple ML systems
• Design and optimize kernels for the TPU
• Provide feedback to researchers on model changes and their impact on performance

Requirements:

• Bachelor's degree or equivalent combination of education, training, and/or experience
• Relevant field of study
• Years of experience required will correlate with the internal job level requirements for the position

Benefits:

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues

Note: This job description is a rewritten version of the original ad, focusing on the key responsibilities, requirements, and benefits.

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Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime.

At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work.

Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that.

To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think.

You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

We're building a new capability at Pinterest: embedding AI-native engineering directly inside our business functions. The AI Solutions Engineer will partner with teams across Marketing, Finance, Sales, HR, Legal, and other functions to surface high-value automation opportunities, then design and ship the AI-powered tools that bring those opportunities to life.

This is a hands-on, mid-level software engineering role for someone who is equally comfortable reading a business process flowchart and writing production-grade Python.

You'll work end-to-end , from discovery and scoping through prototyping, launch, and iteration , using the latest agentic frameworks, tool-calling patterns, and responsible AI practices.

What you'll do:

• Discover and scope AI opportunities: Partner with internal teams across corporate functions to understand their workflows, pain points, and goals, and identify high‑value AI/automation opportunities.
• Map and improve business processes: document current workflows, identify bottlenecks, and propose AI‑enabled changes that deliver clear business outcomes (e.g., time or cost savings, improved quality or compliance).
• Design end-to-end AI solutions: Design and implement AI‑enabled tools and workflows that integrate with existing systems and data sources, and that are intuitive for non‑technical users.
• Build and ship production-quality software: Write clean, maintainable code and tests.
• Pilot, rollout, and drive adoption: Pilot, roll out, and drive adoption of solutions by working closely with end‑users, gathering feedback, and iterating based on real‑world usage.
• Champion for responsible AI: Ensure solutions follow privacy, security, and compliance expectations, especially when working with sensitive or regulated data.
• Build for reuse: Create and share reusable patterns, components, and documentation to make future AI/automation work faster and more consistent across teams.
• Accelerate Workflows with Generative AI and Automation: Leverage AI to accelerate execution (e.g., draft, prototype, outline), explore alternative solutions using AI (iterate, compare approaches), synthesize information with AI (summarize, distill key themes), automate repeatable work (documentation, reporting, QA checks)

What we're looking for:

We're looking for mid-level engineers who have already shipped something real with AI , and who can work as a peer with non-technical business partners, not just as an order-taker.

Specifically, you bring:

• Software engineering foundation. A CS, Engineering, Data Science, or related degree (or equivalent experience), with demonstrated ability to build and operate production systems , backend services, internal tools, integrations, or data applications.
• Hands-on AI and automation delivery. You've shipped AI-powered or automation-driven solutions in a real environment.
• Agentic AI literacy. You understand how modern agentic systems are constructed , the difference between local and remote agents, how MCP (Model Context Protocol) works, what Agent Skills and Hooks are for, and how A2A (Agent-to-Agent) coordination is structured.
• System design and architecture thinking. You can sketch a data flow, reason about integration points, evaluate trade-offs between approaches, and design for failure , including fallbacks, retry logic, timeouts, and human escalation paths.
• Data and security judgment. You understand data access controls, the risks of giving AI broad access to sensitive information, PII minimization, audit logging, and what responsible data handling looks like in an enterprise environment.
• Business function acumen. You can engage credibly with stakeholders in Marketing, Finance, Sales, HR, Legal, or Operations , understanding their workflows, KPIs, and constraints well enough to scope solutions that fit their real needs.
• Clear, collaborative communication. You can explain architecture trade-offs to a Finance Manager and debug a prompt failure with an engineer in the same afternoon.

Preferred Qualifications:

• Experience working embedded with or alongside corporate / G&A functions (Finance, Legal, HR, Marketing, Sales Operations, or similar).
• Practical experience with agentic frameworks such as LangGraph, Claude Agent SDK, or comparable tooling.
• Familiarity with MCP server design , including building, deploying, and securing MCP-compliant tool servers.
• Experience designing and evaluating AI outputs at scale: eval sets, sampling pipelines, human-in-the-loop review queues, or A/B testing of AI-powered features.
• Exposure to responsible AI frameworks: data minimization, differential privacy concepts, model output auditing, or working in PII-sensitive / regulated domains.
• Experience with RAG (Retrieval-Augmented Generation) pipelines, vector databases, or enterprise search integrations.
• Familiarity with CI/CD for AI: prompt versioning, model version pinning, regression testing for LLM-powered features.

Relocation Statement:

This position is not eligible for relocation assistance.

Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

This role will need to be in the office for in-person collaboration 1-2 times every 6-months and therefore can be situated anywhere in the country.

#LI-REMOTE #LI-KBF

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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. The Systems engineering team owns compute uptime and resilience at massive scale, building the clusters, automation, and observability that make frontier AI research possible and safely deployable to customers.

Responsibilities

• Lead infrastructure projects from design through delivery, owning scope, execution, and outcomes
• Build and maintain systems that support AI clusters at massive scale (thousands to hundreds of thousands of machines)
• Partner with cloud providers and internal teams to solve compute, networking, and reliability challenges
• Tackle difficult technical problems in your domain and proactively fill gaps in tooling, documentation, and processes
• Contribute to operational practices including incident response, postmortems, and on-call rotations

Benefits

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues

Requirements

• 6+ years of software engineering experience
• Have led technical projects end-to-end over multiple months, including scoping, breaking down work, and driving delivery
• Have deep knowledge of distributed systems, reliability, and cloud platforms (Kubernetes, IaC, AWS/GCP)
• Are strong in at least one systems language (Python, Rust, Go, Java)
• Solve hard problems independently and know when to pull others in
• Help teammates grow through knowledge sharing and thoughtful technical guidance
• Communicate clearly in design docs, presentations, and cross-functional discussions

Preferred Qualifications

• Security and privacy best practice expertise
• Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Low level systems experience, for example linux kernel tuning and eBPF

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Responsibilities:

• Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
• Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
• Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI
• Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
• Collaborate to translate experimental requirements into production-ready infrastructure
• Develop large scale data pipelines to handle advanced language model training requirements
• Optimize large scale training and inference pipelines for stable and efficient reinforcement learning

You may be a good fit if you:

• Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Are a strong communicator and enjoy working collaboratively
• Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Have proven track record of building large-scale data pipelines and distributed storage systems
• Excel at diagnosing and resolving complex infrastructure challenges in production environments
• Can work effectively across the full ML stack from data pipelines to performance optimization
• Have experience collaborating with other researchers to scale experimental ideas
• Thrive in fast-paced environments and can rapidly iterate from experimentation to production

Strong candidates may also have:

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
• Background in building infrastructure for AI research labs or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Comfort with large scale data pipelines (Beam, Spark, Dask, …)

The annual compensation range for this role is $350,000-$850,000 USD.

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Your work will be critical to delivering a real-world validation of our approach, with a primary focus on core software engineering tasks to:

Enable rapid, trustworthy experimentation. Maintain rigorous benchmarking and testing. Manage scale for both data and model size. Ensure and maintain high data quality for both real-world and synthetic data.

Key Responsibilities

• Design, implement, and maintain robust and reliable systems and workflows for generating large-scale synthetic and real datasets of power grid optimization problems.
• Design and implement rigorous unit, integration, and system tests to ensure the reliability, accuracy, and maintained performance of our models and software, with a focus on data pipelines.
• Maintain and contribute to our machine learning codebase, ensuring efficient data structures and seamless integration with our power system models and optimization solvers.
• Ensure the codebase supports ongoing experimentation, while simultaneously increasing scalability, robustness, and reliability via improved integration testing and performance benchmarking.
• Work closely and collaboratively with a team of engineers, research scientists, and product managers to deliver real-world impact.

Minimum Qualifications

• Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience.
• Excellent proficiency in C++, Python, or Jax.
• Demonstrated experience developing or utilizing solutions for robustness or quality assurance within software and/or ML systems.
• Experience processing, generating, and analyzing large-scale data, e.g. for ML applications.
• Proven ability to discuss technical ideas effectively and collaborate in interdisciplinary teams.
• Motivated by the prospect of real-world impact and focused on excellence in software development.

Preferred Qualifications

• Experience with Google's technical stack and/or Google Cloud Platform (GCP).
• Familiarity with modern hardware accelerators (GPU / TPU).
• Experience with modern ML training frameworks, such as Jax.
• Experience in developing software in a translational research or production setting.
• Proficiency in Julia

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As an Engineering Manager, you will own the end-to-end product of Claude on AWS, including API, load balancing, inference, capacity and operations. Your team will ensure our LLMs meet rigorous performance, safety and security standards and enhance our core infrastructure for packaging, testing, and deploying inference technology across the globe.

Responsibilities:

• Set technical strategy and oversee development of Claude on AWS across all layers of the technical stack.
• Collaborate across teams and companies to deeply understand product, infrastructure, operations and capacity needs, identifying potential solutions to support frontier LLM serving
• Work closely with cross-functional stakeholders across companies to align on goals and drive outcomes
• Create clarity for the team and stakeholders in an ambiguous and evolving environment
• Take an inclusive approach to hiring and coaching top technical talent, and support a high performing team
• Design and run processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively and never fail the same way twice

Requirements:

• 10+ years of experience in high-scale, high-reliability software development, particularly infrastructure or capacity management
• 5+ years of engineering management experience
• Experience recruiting, scaling, and retaining engineering talent in a high growth environment
• Have experience scaling products, resources and operations to accommodate rapid growth
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships and strategy with stakeholders across engineering, product, finance, and sales
• Have experience working with external partners to align goals and deliver impact
• Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space
• Have excellent written and verbal communication skills
• Demonstrated success building a culture of belonging and engineering excellence
• Are motivated by developing AI responsibly and safely
• Are willing and able to travel frequently between Seattle and the SF Bay Area

Strong candidates may also have experience with:

• Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Experience as a Product Manager
• Experience with deployment and capacity management automation
• Security and privacy best practice expertise

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

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You will track and interpret key economic indicators across regions, assess the implications of monetary and fiscal policy decisions, and evaluate how macro trends shape financial markets and risk conditions.

This role sits at the intersection of economics, markets, and strategic decision-making.

You will build forecasts and scenario analyses to understand how shifts in growth, inflation, interest rates, currencies, commodities, and geopolitics may affect asset prices and broader market regimes.

Your work will inform top-down investment views and risk assessments, supporting global macro strategies driven by economic fundamentals rather than individual company analysis.

The ideal candidate is intellectually curious, analytically rigorous, and comfortable working in a fast-moving, high-ownership environment.

Consistent with xAI's flat organisational structure, you are expected to be hands-on, proactive, and able to communicate ideas clearly and concisely to a highly technical and motivated team.

As a Macro Research Analyst, you will contribute directly to this mission by helping the organisation understand large-scale economic systems, feedback loops, and regime shifts that shape global outcomes.

You will structure, analyse, and interpret macroeconomic data across countries and regions, transforming noisy and often incomplete information into coherent economic narratives and testable hypotheses.

Your research will help identify causal relationships, constraints, and second-order effects within complex economic systems,insights that are essential for training, evaluating, and applying advanced AI models to real-world economic and financial questions.

By developing forecasts, scenarios, and risk assessments, you will support xAI's efforts to reason about uncertainty, stress-test assumptions, and evaluate how shocks propagate through global markets and institutions.

Clear communication of your findings will ensure that economic insights are effectively shared across teams, reinforcing xAI's emphasis on rigorous thinking, transparency, and collaborative problem-solving.

In this role, macroeconomic research is not an end in itself, but a tool for advancing deeper understanding.

Your work will help xAI build systems that reason more accurately about economic behaviour, policy trade-offs, and global dynamics,bringing us closer to AI that can meaningfully assist human decision-making at scale.

The Macro Research Analyst will have broad responsibility for analysing global macroeconomic conditions and their implications for financial markets and strategic decision-making.

The scope of the role spans data analysis, economic modelling, and synthesis of macroeconomic developments across multiple regions and asset classes.

The role includes management of ongoing monitoring frameworks for key economic indicators, policy developments, and geopolitical risks.

The analyst will be expected to independently identify emerging themes, develop forward-looking scenarios, and assess how macroeconomic shifts may alter market dynamics, risk exposures, and opportunity sets.

The position requires close collaboration with internal stakeholders, with an emphasis on clear, concise communication of complex economic concepts.

Given xAI's flat organisational structure, the analyst will operate with a high degree of autonomy and accountability, contributing directly to research outputs and strategic discussions rather than functioning in a narrow support capacity.

The scope of the role is intentionally flexible, allowing a high-performing analyst to expand their responsibilities over time by taking ownership of new research areas, developing novel analytical frameworks, and influencing how macroeconomic intelligence is integrated into xAI's broader mission.

Responsibilities:

Monitor and interpret data such as GDP growth, inflation, unemployment rates, interest rates, fiscal/monetary policy, currency movements, commodity prices, and geopolitical events across countries or regions.

Build economic forecasts, scenario analysis, and models to predict how macro forces will affect markets (e.g., how a central bank rate hike might impact equities, bonds, or currencies).

Provide research reports, trade ideas, and recommendations to portfolio managers, traders, or clients. This often informs 'global macro' investment strategies, where decisions are driven primarily by top-down economic views rather than individual company fundamentals.

Identify potential risks (e.g., recessions, currency crises, trade wars) and opportunities arising from economic shifts.

Basic Qualifications:

Strong academic background in economics, finance, mathematics, statistics, or a related quantitative field.

Demonstrated experience in macroeconomic research, global macro investing, economic policy analysis, or a closely related role.

Deep understanding of macroeconomic indicators and frameworks, including growth, inflation, labor markets, monetary and fiscal policy, exchange rates, commodities, and geopolitical dynamics.

Experience building and interpreting economic models, forecasts, and scenario analyses to assess market and policy outcomes.

Ability to synthesise large and complex data sets into clear, structured insights and actionable conclusions.

Strong written and verbal communication skills, with the ability to explain complex economic concepts concisely to both technical and non-technical audiences.

High level of intellectual curiosity and comfort working with uncertainty, incomplete information, and competing hypotheses.

Self-directed, detail-oriented, and capable of operating effectively in a flat organisational structure with minimal oversight.

Proven ability to prioritise effectively, manage multiple research streams, and deliver high-quality work under time constraints.

Preferred Skills and Experience:

3–7 years of experience post-doc.

At least three publications in reputable economics journals (AER, QRE, JPE, Econometrica, etc.) or outlets (The Economist, The Wall Street Journal, Financial Times, etc.).

Prior experience at a global macro hedge fund, asset manager, central bank, international financial institution, policy research organisation, or economic consulting firm.

Strong familiarity with financial markets across multiple asset classes, including rates, FX, equities, and commodities.

Experience producing investment-oriented research, including trade ideas, market commentary, or risk assessments tied to macroeconomic views.

Proficiency with data analysis and modelling tools such as Python, R, MATLAB, or similar quantitative environments.

Experience working with large economic and financial datasets and building repeatable research pipelines.

Ability to evaluate tail risks, regime shifts, and non-linear dynamics in macroeconomic systems.

Interest in applying macroeconomic reasoning to novel problems, including AI-driven applications.

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In this role, you will contribute high-quality accounting analysis, explanations, and evaluations that support the training of AI models on complex accounting concepts.

Working in close collaboration with technical and research teams, you will help shape new AI capabilities by evaluating sophisticated accounting scenarios and guiding how models reason through them.

You will draw on deep knowledge of financial reporting, consolidations, internal controls, and U.S. GAAP to identify and analyze challenging accounting problems comparable to upper-division or graduate-level coursework.

Your input will directly influence how effectively AI systems interpret accounting standards and communicate sound accounting judgments.

This role is well suited for faculty who thrive in intellectually dynamic environments and are comfortable engaging with evolving prompts, frameworks, and research objectives.

Responsibilities:

• Use proprietary software applications to provide input/labels on defined projects.
• Support and ensure the delivery of high-quality curated data.
• Play a pivotal role in supporting and contributing to the training of new tasks, working closely with the technical staff to ensure the successful development and implementation of cutting-edge initiatives/technologies.
• Interact with the technical staff to help improve the design of efficient annotation tools.
• Choose problems from corporate accounting fields that align with your expertise, providing rigorous solutions and model critiques where you can confidently provide detailed solutions and evaluate model responses.
• Regularly interpret, analyze, and execute tasks based on given instructions.

Basic Qualifications:

• PhD or DBA in Accounting, or a Master’s degree in Accounting combined with a CPA license and substantial professional experience.
• Ability to analyze and explain complex GAAP issues clearly and rigorously, consistent with upper-division or graduate-level instruction.
• Strong written communication skills in English, with the ability to articulate accounting judgments in both formal, technical contexts and instructional explanations.
• Familiarity with authoritative accounting and financial reporting resources, such as FASB Codification, SEC EDGAR, and public-company financial statements.
• Excellent analytical judgment, intellectual curiosity, and capacity to reason independently in situations involving ambiguity or incomplete information.
• Demonstrated interest in emerging technologies and innovation in accounting education, research, or practice.

Preferred Skills and Experience:

• Demonstrated expertise in corporate financial accounting and reporting, grounded in professional practice within public accounting or SEC-reporting organizations, with experience addressing complex transactions, consolidations, or judgment-intensive accounting matters.
• Experience authoring or reviewing technical accounting analyses, including research memoranda, disclosure analyses, or case-based materials.
• Peer-reviewed publication(s) in academic or practitioner-oriented accounting journals, or comparable scholarly output.
• Teaching experience at the undergraduate or graduate level in financial accounting, intermediate/advanced accounting, auditing, or related subjects.

Location and Other Expectations:

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit.
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required.
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs.
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time.
• We are unable to provide visa sponsorship.
• For those who will be working from a personal device, your computer must be a Chromebook, Mac with MacOS 11.0 or later, or Windows 10 or later.

Compensation and Benefits:

US based candidates: $45/hour - $100/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications. International candidates: Information will be provided to you during the recruitment process. Benefits vary based on employment type, location and jurisdiction. Benefits for eligible U.S. based positions include health insurance, 401(k) plan, and paid sick leave. Specific details and role specific information will be provided to you during the interview process. xAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

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Your work will focus on curating and annotating high-quality audio data to enhance Grok's global accessibility, enabling natural spoken interactions for users worldwide, bridging language barriers through accurate speech processing, and improving the AI's handling of multilingual audio nuances.

Responsibilities: Use proprietary software to provide labels, annotations, recordings, and inputs on projects involving multilingual audio clips, voice recordings, speech samples, and auditory elements in various languages. Support the delivery of high-quality curated audio data that ensures clear, natural spoken output, accurate representation of linguistic and prosodic details (such as intonation, rhythm, and accent), and professional audio standards. Collaborate with technical staff to develop tasks that improve AI's ability to handle speech modulation, accent variation, noise in real-world recordings, and multilingual audio processing. Work with technical staff to improve annotation tools for efficient audio workflows.

Basic Qualifications: Native proficiency in Hindi with exposure to diverse accents, dialects, or regional variations. Proficiency in English (minimum B2 level) with clear, natural vocal delivery and pronunciation suitable for audio recording purposes. Strong auditory perception to identify nuances in speech, accents, pronunciation, intonation, and audio quality across languages. Demonstrated ability to handle multilingual audio content, including evaluating speech accuracy, cultural vocal expressions, and contextual interpretation in spoken form. Demonstrated ability to transcribe audio with high accuracy across accents and varying audio quality. Comfort providing high-quality voice recordings and feedback on audio samples in multiple languages. Strong comprehension skills and the ability to make independent judgments on ambiguous or varied audio material, including noisy or accented speech. Strong communication, interpersonal, analytical, detail-oriented, and organizational skills, with the ability to articulate audio-related feedback effectively. Commitment to developing AI that masters sophisticated multilingual audio capabilities.

Preferred Skills and Experience: Demonstration of exceptional attention to linguistic nuance, auditory detail, and data quality beyond standard transcription work. Deep understanding and taste of what good/useful Audio data is. Strong command of advanced transcription and annotation practices, including handling disfluencies, accents, and prosodic features (intonation, stress, rhythm, emotion, etc) with high consistency and accuracy. Background in linguistics (e.g., phonetics, phonology, sociolinguistics), speech sciences, cognitive science, or a related field, or equivalent practical experience, with demonstrated ability to analyze accent variation, pronunciation differences, and multilingual speech patterns. Experience working with speech/audio datasets, annotation workflows, or AI training data, including knowledge/experience with training voice models, and an understanding of how data quality impacts model performance. Professional experience in voice work, including voice acting, voice recording, podcasting with a measurable audience (e.g., X following), or similar audio production demonstrating attention to clarity and recording quality. Demonstrated ability to exercise independent judgment in ambiguous audio scenarios and make consistent, defensible annotation decisions. Portfolio (strongly preferred for advanced candidates): Voice samples, annotated transcripts, or audio-related work demonstrating quality, methodology, and attention to detail. Candidates with professional experience in voice, linguistics, speech data, or speech evaluation and research are especially encouraged to apply.

Location and Other Expectations: Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit. For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required. On average, most projects may require at least 10 hours per week to deliver effectively, though this is not a fixed commitment and depends on the scope of work. Contractors have full flexibility to set their own hours and determine the exact amount of time needed to complete deliverables. Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role-specific needs. For US-based candidates, please note that we are unable to hire in Wyoming and Illinois at this time. We are unable to provide visa sponsorship. For those who will be working from a personal device, your computer must be a Chromebook, a Mac with macOS 11.0 or later, or Windows 10 or later.

Compensation and Benefits: US-based candidates: $35/hour - $45/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications. International candidates: Information will be provided to you during the recruitment process. Benefits vary based on employment type, location, and jurisdiction. Benefits for eligible U.S.-based positions include health insurance, 401(k) plan, and paid sick leave. Specific details and role-specific information will be provided to you during the interview process.

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You will focus on large molds: loading dry material & stringers, installing vacuum manifolds, bagging, drop tests, leak detection, maintaining vacuum catch pots, mixing resin and MEKP to appropriate ratios, and other infusion techniques.

This hands-on role is critical to our manufacturing process and directly impacts product reliability and performance.

Key Responsibilities:

• Attention to detail and commitment to industry-leading standards
• Prepare molds and tooling for composite layup and infusion
• Perform precise hand layups using dry fiber materials
• Set up and execute vacuum-assisted resin infusion processes
• Mix and handle resins, hardeners, and other composite materials in accordance with safety protocols
• Ensure each fiberglass layer is installed to prevent bridging, wrinkling, and resin richness
• Install core with minimal gaps
• Maintain a clean, organized, and safety-compliant workspace
• Interpret technical drawings, layup schedules, and process documentation using a tablet
• Collaborate closely with engineering and quality teams to iterate on part design and manufacturability
• Document work performed and identify process improvements or quality issues
• Work with a sense of urgency
• Mix putty, debur surfaces to prep for dry loading fiberglass and duties as assigned

Requirements:

• 2+ years of hands-on experience with composites manufacturing, particularly resin infusion at a high rate boat building factory
• Strong attention to detail, manual dexterity, and a quality-first mindset
• Experience with vacuum bagging hull and decks
• Ability to work with minimal supervision in a fast-paced, iterative environment
• Familiarity with vinyl ester and polyester resins, carbon fiber, dry fiberglass
• Comfortable using trimming tools, grinders, and measurement instruments
• Able to read technical drawings and follow standard operating procedures
• U.S. Person (citizen or permanent resident) required due to DoD contract work

Preferred Qualifications:

• Ability to use a MVP Hight Output System
• Experience with marine composite fabrication
• Familiarity with AS9100 or ISO 9001 quality systems
• Forklift or crane certification a plus

Additional Information

Benefits: Medical Insurance: Comprehensive health insurance plans covering a range of services Saronic pays 100% of the premium for employees and 80% for dependents Dental and Vision Insurance: Coverage for routine dental check-ups, orthodontics, and vision care Saronic pays 100% of the premium under the basic plan for employees and 80% for dependents Time Off: Generous PTO and Holidays Parental Leave: Paid maternity and paternity leave to support new parents Competitive Salary: Industry-standard salaries with opportunities for performance-based bonuses Retirement Plan: 401(k) plan with company match Stock Options: Equity options to give employees a stake in the company’s success Life and Disability Insurance: Basic life insurance and short- and long-term disability coverage Pet Insurance: Discounted pet insurance options including 24/7 Telehealth helpline Additional Perks: Free lunch benefit and unlimited free drinks and snacks in the office

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On this team, you will build and operate the ML infrastructure and product services that enable trust and intelligence across Plaid's network. You'll own feature engineering, offline training and batch scoring, online feature serving, and real-time inference so model outputs directly power partner-facing fraud & trust products and bank intelligence features.

Responsibilities

• Embed model inference into Network Enablement product flows and decision logic (APIs, feature flags, backend flows).
• Define and instrument product + ML success metrics (fraud reduction, retention lift, false positives, downstream impact).
• Design and run experiments and rollout plans (backtesting, shadow scoring, A/B tests, feature-flagged releases) to validate product hypotheses.
• Build and operate offline training pipelines and production batch scoring for bank intelligence products.
• Ship and maintain online feature serving and low-latency model inference endpoints for real-time partner/bank scoring.
• Implement model CI/CD, model/version registry, and safe rollout/rollback strategies.
• Monitor model/data health: drift/regression detection, model-quality dashboards, alerts, and SLOs targeted to partner product needs.
• Ensure offline and online parity, data lineage, and automated validation / data contracts to reduce regressions.
• Optimize inference performance and cost for real-time scoring (batching, caching, runtime selection).
• Ensure fairness, explainability and PII-aware handling for partner-facing ML features; maintain auditability for compliance.
• Partner with platform and cross-functional teams to scale the ML/data foundation (graph features, sequence embeddings, unified pipelines).
• Mentor engineers and document team standards for ML productization and operations.

Qualifications

• Must-haves:
• Strong software engineering skills including systems design, APIs, and building reliable backend services (Go or Python preferred).
• Production experience with batch and streaming data pipelines and orchestration tools such as Airflow or Spark.
• Experience building or operating real-time scoring and online feature-serving systems, including feature stores and low-latency model inference.
• Experience integrating model outputs into product flows (APIs, feature flags) and measuring impact through experiments and product metrics.
• Experience with model lifecycle and operations: model registries, CI/CD for models, reproducible training, offline & online parity, monitoring and incident response.
• Nice to have:
• Experience in fraud, risk, or marketing intelligence domains.
• Experience with feature-store products (Tecton / Chronon / Feast / internal) and unified pipelines.
• Experience with graph frameworks, graph feature engineering, or sequence embeddings.
• Experience optimizing inference at scale (Triton/ONNX/quantization, batching, caching).

Additional Information

Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable.

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At Mistral, we are on a mission to democratize AI, producing frontier intelligence for everyone, developed in the open, and built by engineers all over the world.

We develop models for the enterprise and for consumers, focusing on delivering systems which can really change the way in which businesses operate and which can integrate into our daily lives. All while releasing frontier models open-source, for everyone to try and benefit.

What will you do?

• Research and develop novel methods to push the frontier of large language models
• Work across use cases (e.g reasoning, code, agents) and modalities (e.g text, image and speech)
• Build tooling and infrastructure to allow training, evaluation and analysis of AI models at scale
• Work cross-functionally with other scientists, engineers and product teams to ship AI systems which have a real-world impact

About you

• An expert in speech input/output methodologies (specific to audio)
• Highly proficient software engineer in at least one programming language (Python or other, e.g. Rust, Go, Java)
• Hands-on experience with AI frameworks (e.g. PyTorch, JAX) or distributed systems (e.g. Ray, Kubernetes)
• High engineering competence. This means being able to design complex software and make it usable in production
• Self-starter, autonomous and a team player

Now, it would be ideal if

• You have experience working with large-scale speech-language models
• You have hands-on experience with training large transformer models in a distributed fashion
• You can navigate the full MLOps stack, for instance, fine-tuning, evaluation and deployment
• You have a strong publication record in a relevant scientific domain

Benefits

France

• Competitive cash salary and equity
• Food: Daily lunch vouchers
• Sport: Monthly contribution to a Gympass subscription
• Transportation: Monthly contribution to a mobility pass
• Health: Full health insurance for you and your family
• Parental: Generous parental leave policy

UK

• Competitive cash salary and equity
• Insurance
• Transportation: Reimburse office parking charges, or 90GBP/month for public transport
• Sport: 90GBP/month reimbursement for gym membership
• Meal voucher: £200 monthly allowance for its meals
• Pension plan: SmartPension (percentages are 5% Employee & 3% Employer)

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This is a backend engineering role at its core, we're looking for a strong backend engineer who knows how to work effectively with AI tools, understands where AI can accelerate development and product capabilities, and is comfortable integrating AI-powered features into production backend systems.

Given the global nature of our business and the collaborative nature of our team, fluency in English is required for daily work with engineering, product, and business teams across multiple regions. Fluency in Spanish or Portuguese is equally required , our LATAM teams, customers, and operational partners work in both languages, and you will too.

Responsibilities

• Backend Engineering - Design, build, and maintain scalable, reliable backend services that process financial transactions and serve Jeeves customers across 20+ countries.
• Write clean, testable, production-quality code in Go, Python, or Node.js/TypeScript; participate actively in design and code reviews.
• Build and consume RESTful and GraphQL APIs; design inter-service communication using gRPC, message queues, and event-driven architectures.
• Design and optimize relational and non-relational database schemas (PostgreSQL, MongoDB, Redis) for correctness, performance, and scale.
• Own backend features end-to-end , from scoping and technical design through deployment, monitoring, and iteration.
• Implement security best practices: authentication, authorization, input validation, and data protection across distributed services.

AI-Assisted Feature Development

• Integrate LLM API calls (e.g., OpenAI, Anthropic) into backend services as product features , such as spend categorization, document parsing, or natural language workflows , ensuring those integrations are reliable, observable, and cost-efficient.
• Build backend pipelines that consume AI-generated outputs safely: validate structured outputs, handle fallback scenarios, and design graceful degradation when AI services are unavailable or return low-confidence results.
• Collaborate with AI and data science teams to integrate model outputs into backend APIs , bridging experimental AI work and production systems.
• Use AI coding tools (GitHub Copilot, Claude, Cursor, etc.) fluently as part of your everyday development workflow.

Reliability & Operations

• Instrument services with structured logging, distributed tracing, and metrics for full operational visibility.
• Participate in on-call rotation; respond to production incidents and contribute to post-incident reviews.
• Contribute to CI/CD pipeline improvements, testing infrastructure, and deployment practices.

Cross-Regional Collaboration

• Work closely with engineering, product, compliance, and data teams across multiple time zones and regions , communicating in both English and Spanish or Portuguese as the situation requires.
• Contribute to a globally distributed engineering culture through thorough documentation, async design reviews, and thoughtful pull request feedback.
• Bring your regional perspective to product and engineering conversations , our LATAM customers have specific needs, and engineers who understand those markets make our product better.

Requirements

• 5+ years of professional backend engineering experience building and operating production systems.
• Fluent in English , professional fluency required for daily work with global teams in written and spoken contexts.
• Fluent in Spanish or Portuguese , required for collaboration with LATAM teammates, customers, and operational partners.
• Strong proficiency in at least one backend language: Go, Python, or Node.js/TypeScript.
• Experience designing and building RESTful APIs, microservices, and event-driven backend systems.
• Solid understanding of relational databases (PostgreSQL preferred): schema design, query optimization, and data modeling.
• Experience with cloud infrastructure (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines.
• Demonstrated ability to integrate third-party APIs reliably in production , including error handling, retry logic, and observability.
• Experience working on globally distributed teams across time zones and regions.
• Comfortable using AI tools as part of everyday engineering work , integrating LLM API outputs into backend services and using AI coding assistants fluently.

Preferred Qualifications

• Experience in fintech, financial services, payments, or a regulated industry , familiarity with ledger systems, payment rails, or financial compliance (KYC/AML, PCI-DSS) is a strong plus.
• Prior experience at a startup or high-growth scale-up, comfortable building in ambiguity without heavy process support.
• Experience with multi-currency systems or cross-border payment processing.
• Familiarity with message queue systems (Kafka, RabbitMQ) and event-driven architecture.
• Global work experience , prior roles at companies operating across multiple countries and regulatory environments.
• Fluency in both Spanish and Portuguese.

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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As a Product Lead, you will set the strategic direction for how we engage with protocol ecosystems by defining the frameworks that guide which networks we support, how deeply we integrate, and what differentiated value we deliver.

You will drive cross-functional alignment at the senior level, and shape a roadmap that positions Anchorage as the default institutional partner for the most important networks in crypto.

This is a role for someone who thrives at the intersection of product vision, people leadership, and deep protocol fluency.

Technical Skills:

• Drive end-to-end product strategy for the Anchorage’s engagement with protocol ecosystems, balancing near-term commitments with long-term platform investments.
• Develop and maintain a strategic lens on the blockchain landscape to identify which ecosystems, consensus mechanisms, and on-chain primitives warrant investment and how to sequence them.
• Drive comprehensive go-to-market strategy alongside sales and partnerships, defining success metrics and iterating based on data and market feedback.
• Demonstrate the ability to understand system architecture deeply enough to set sound technical direction with engineering leadership.

Complexity and Impact of Work:

• Drive cross-functional initiatives that span engineering, design, sales, compliance, and external partners while resolving ambiguity and maintaining momentum across multiple workstreams.
• Contribute strategic insights that shape company-level priorities and influence leadership decisions beyond your immediate team.

Organizational Knowledge:

• Develop and leverage a deep understanding of Anchorage's business model, regulatory landscape, and competitive positioning to inform ecosystem strategy.
• Build trusted relationships with senior leaders across departments to ensure alignment on priorities, surfacing trade-offs early, and driving shared accountability.
• Navigate and improve organizational processes to increase the Protocols team's efficiency, predictability, and quality of output.

Communication and Influence:

• Represent the Protocols organization to senior leadership, the board, and external partners, articulating strategy, progress, and trade-offs with clarity and conviction.
• Enable cross-functional collaboration through consistent, transparent communication and thoughtful influence on decision-making at every level.
• Serve as a knowledge partner to leadership on blockchain ecosystem trends, helping the company stay ahead of market shifts.

You may be a fit for this role if you have:

• You have 7+ years of product management experience, with at least 2 years leading or managing other product managers.
• You have meaningful experience in blockchain, DeFi, or protocol-adjacent companies, and you understand the nuances of ecosystem dynamics, not just the technology.
• You have a track record of defining and executing multi-quarter product strategies that required navigating ambiguity, competing priorities, and cross-functional dependencies.
• You have a background in engineering or a demonstrated ability to go deep on technical architecture when the situation demands it.
• Exceptional written and verbal communication skills, enabling you to distill complex ecosystem trade-offs into crisp narratives for leadership, partners, and your team.
• Your empathy and adaptability not only complement others' working styles but also embody our culture of curiosity, creativity, and shared understanding.
• You are deeply invested in optimizing the end-user experience and leveraging it to create business value.
• You self describe as some combination of the following: creative, humble, ambitious, detail oriented, hard working, trustworthy, eager to learn, methodical, action oriented, and tenacious.

Although not a requirement, bonus points if:

• You have shipped products that required direct collaboration with protocol foundations or core development teams.
• You have built or contributed to ecosystem evaluation frameworks, partnership models, or integration playbooks at a previous company.
• You have written your own smart contract or dApp.
• You have experience managing P&L accountability or revenue targets tied to product decisions.

Additional Information About Anchorage Digital:

Who we are

The Anchorage Village, what we call our team, brings together the brightest minds from platform security, financial services, and distributed ledger technology to provide the building blocks that empower institutions to safely participate in the evolving digital asset ecosystem.

As a diverse team of more than 600 members, we are united in one common goal: building the future of finance by providing the foundation upon which value moves safely in the new global economy.

Anchorage Digital is committed to being a welcoming and inclusive workplace for everyone, and we are intentional about making sure people feel respected, supported, and connected at work,regardless of who you are or where you come from.

We value and celebrate our differences and we believe being open about who we are allows us to do the best work of our lives.

Anchorage Digital is an Equal Opportunity Employer.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or veteran status.

Anchorage Digital considers qualified applicants with criminal histories, consistent with applicable federal, state, and local laws.

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You will collaborate with IT and engineering teams to establish and track patching and remediation priorities, focusing on CMMC scoring impact. You will maintain ownership of all Plans of Action and Milestones (POA&Ms), validating remediation closure evidence, and ensuring alignment with DoD and CMMC requirements. You will also support continuous control monitoring activities for ongoing compliance between assessments.

As a Cyber Security Engineer, you will communicate compliance posture, risks, and remediation status to both technical and non-technical audiences, and support user and stakeholder education. You will also escalate unresolved compliance or remediation risks to cybersecurity and audit leadership as appropriate.

This is an exciting opportunity to join a driven and collaborative Cybersecurity team at Synopsys, working alongside experts in IT, Engineering, and Business Operations. You will report to the Executive Director of Cybersecurity and play a central role in audit readiness, evidence management, and cross-functional collaboration.

To be successful in this role, you will need:

• Security+ (SEC+) or equivalent industry-recognized cybersecurity certification
• 4+ years of experience performing Information Assurance, ISSO, ISSE, or equivalent cybersecurity assurance functions
• 2+ years supporting cybersecurity operations in a DoD or defense-adjacent enterprise environment
• Experience supporting NIST SP 800-171, RMF-aligned, or CMMC-related compliance activities
• Ability to obtain and maintain a U.S. DoD, FBI, or DHS security clearance
• Strong technical understanding of modern hardware, software, and enterprise infrastructure environments
• Familiarity with vulnerability management platforms, compliance evidence repositories, and security monitoring outputs
• Excellent organizational, prioritization, and time-management skills
• Strong analytical and problem-solving abilities with attention to detail
• Ability to work effectively across technical and non-technical teams to resolve complex compliance issues
• Strong written and verbal communication skills, including the ability to present information to leadership and stakeholder groups
• Demonstrated ability to manage multiple competing priorities in a high-assurance environment

If you are a collaborative team player who thrives in cross-functional environments, detail-oriented and diligent, proactive and resourceful, clear communicator who can translate technical concepts to non-technical audiences, analytical thinker with strong problem-solving skills, adaptable and resilient, and ethical and trustworthy, committed to maintaining high standards of integrity and confidentiality, then we encourage you to apply for this exciting opportunity.

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Your key responsibilities will include:

Designing, implementing, and maintaining robust and reliable systems and workflows for generating large-scale synthetic and real datasets of power grid optimization problems.

Designing and implementing rigorous unit, integration, and system tests to ensure the reliability, accuracy, and maintained performance of our models and software, with a focus on data pipelines.

Maintaining and contributing to our machine learning codebase, ensuring efficient data structures and seamless integration with our power system models and optimization solvers.

Ensuring the codebase supports ongoing experimentation, while simultaneously increasing scalability, robustness, and reliability via improved integration testing and performance benchmarking.

Working closely and collaboratively with a team of engineers, research scientists, and product managers to deliver real-world impact.

To be successful in this role, you will need:

A Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience.

Excellent proficiency in C++, Python, or Jax.

Demonstrated experience developing or utilizing solutions for robustness or quality assurance within software and/or ML systems.

Experience processing, generating, and analyzing large-scale data, e.g. for ML applications.

Proven ability to discuss technical ideas effectively and collaborate in interdisciplinary teams.

Motivated by the prospect of real-world impact and focused on excellence in software development.

Preferred qualifications include experience with Google's technical stack and/or Google Cloud Platform (GCP), familiarity with modern hardware accelerators (GPU / TPU), experience with modern ML training frameworks, such as Jax, and experience in developing software in a translational research or production setting.

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As a Chemist in the Responsible Development & Innovation (ReDI) team at Google DeepMind, you will be a principal architect of the safety protocols governing the intersection of Large Language Models (LLMs) and the chemical sciences. You will design and execute rigorous safety evaluations and inform mitigation strategies that ensure our frontier models accelerate scientific discovery without compromising global security.

This role is pivotal in deciding when and how our most advanced AI systems are released to the world.

You will apply your knowledge of chemistry to devise evaluation methodologies (e.g. red-teaming, knowledge elicitation studies, etc.) and contribute to building and running these evaluations on new models. You will analyse the results from evaluations, communicate them clearly to advise and inform decision-makers on the safety of our AI systems, and use them to refine our harm frameworks and inform our mitigation strategies.

In this role, you will work closely with other Subject-Matter Experts (SMEs) in the chemical, biological, radiological and nuclear domains, Research Engineers and Research Scientists focused on developing AI systems, as well as experts in AI ethics and policy.

Key Responsibilities:

• Architect of Safety Evaluations: Build rigorous, scalable frameworks to evaluate model proficiency in overcoming key bottlenecks in CWA precursor acquisition, chemical synthesis, and weaponisation.
• Strategic Advisory: Analyse evaluation results to brief executive decision-makers on model safety, directly influencing deployment 'Go/No-Go' decisions.
• Harm Framework Innovation: Refine our internal safety taxonomies to account for emergent risks at the intersection of general AI and specialist models like AlphaFold.
• Collaborative Mitigation: Partner with Research Engineers to revise mitigation strategies and refine harm frameworks for identified chemical risks. Work with other SMEs in the chemical, biological, radiological, nuclear, and conventional explosive domains to build a unified defence against CBRNE-related risks.
• External Engagement: Stay abreast of global chemical security trends and international non-proliferation policy through engagement with external international, governmental, and non-governmental organisations.

About You:

You are a seasoned scientist who bridges the gap between laboratory chemistry and emerging technology. You are motivated by the challenge of defending complex systems and possess the critical mindset required to anticipate non-obvious misuse scenarios.

Minimum Qualifications:

• Chemistry Expertise: PhD in synthetic organic chemistry with at least two years post-doctoral or equivalent experience.
• Publication Record: Proven experience publishing as a first author in high-impact general science or chemistry-specific journals, and presenting work at international chemistry conferences. Classified or internal reporting experience will be considered in lieu of public records for candidates from roles in national security.
• Security Domain Expertise: Comprehensive understanding of the Chemical Weapons Convention (CWC) and other national and international CWA agreements/treaties, chemical defence protocols, and the landscape of dual-use research in the chemical domain.
• Systems Thinking: The ability to translate high-level chemical risks into technical requirements for AI safety.
• Communication Excellence: A proven ability in distilling complex technical findings into clear, actionable advice for non-specialist stakeholders.

Preferred Experience:

• Knowledge of CWA defence, including synthesis, detection, and countermeasures.
• Direct experience with CBRNE mitigation, non-proliferation, or relevant international security stakeholders.
• Familiarity with the machine learning lifecycle and AI Safety Frameworks.
• Experience using and/or developing computational chemistry tools (e.g., AlphaFold, retrosynthesis engines, etc.).
• Working knowledge of the Frontier Safety Framework (FSF), Critical Capability Levels (CCLs), and similar documents published by other leading AI labs.
• Understanding of Google DeepMind AI research output (e.g., AlphaFold, GNoME, WeatherNext, etc.), and AI products (e.g., Gemini, Nano Banana, Genie, etc.).
• Passion for the ethical deployment of frontier technologies and AI policy.

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The Hugging Face ML Cloud team is working through strategic collaborations with the most used Clouds (AWS, GCP, Azure, Cloudflare), AI Accelerators (incl. NVIDIA, AMD, Intel, Gaudi, Inferentia, TPU), and Systems (Dell, Nutanix), to make it easy for the community to use Hugging Face models and libraries on these compute platforms.

This role is not a marketing role, or a business development role. Your impact will be driving visibility and usage of integrations with strategic partners, through activities including:

• Publishing technical blog posts
• Contributing documentation and code examples
• Speaking to business and technical audiences at partner conferences,
• Participating in, or producing webinars
• Building and evangelizing demos
• Leading GTM conversations with strategic partners.

You will be at the forefront of Generative AI (and how to build practical stuff with open source). You will work hand in hand with the most important companies in AI. You will enjoy a lot of autonomy and full creative control, with the goal to have 10x more impact than a similar role at a big tech corporation.

About You

You are passionate about ML Engineering, building practical AI applications, putting them in production, and accelerating them to the best of the Cloud ability. You love learning new challenging engineering concepts and technologies, and discussing them with engineers. You appreciate a good Developer Experience, and take pride in your code being easy to understand. You are a great communicator and educator, comfortable (as much as one can be!) with public speaking to technical audiences. You love engaging with the ML community in a positive and helpful way. Existing engagement in social platforms (GitHub, LinkedIn, Twitter, Reddit, etc) or other communication/education channels is expected. Having experience in Open Source development will be helpful.

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At Mistral, we are on a mission to democratize AI, producing frontier intelligence for everyone, developed in the open, and built by engineers all over the world.

We develop models for the enterprise and for consumers, focusing on delivering systems which can really change the way in which businesses operate and which can integrate into our daily lives. All while releasing frontier models open-source, for everyone to try and benefit.

What will you do?

• Research and develop novel methods to push the frontier of large language models
• Work across use cases (e.g reasoning, code, agents) and modalities (e.g text, image and speech)
• Build tooling and infrastructure to allow training, evaluation and analysis of AI models at scale
• Work cross-functionally with other scientists, engineers and product teams to ship AI systems which have a real-world impact

About you

• An expert in speech input/output methodologies (specific to audio)
• You are a highly proficient software engineer in at least one programming language (Python or other, e.g. Rust, Go, Java)
• You have hands-on experience with AI frameworks (e.g. PyTorch, JAX) or distributed systems (e.g. Ray, Kubernetes)
• You have high engineering competence. This means being able to design complex software and make it usable in production
• You are a self-starter, autonomous and a team player

Now, it would be ideal if

• You have experience working with large-scale speech-language models
• You have hands-on experience with training large transformer models in a distributed fashion
• You can navigate the full MLOps stack, for instance, fine-tuning, evaluation and deployment
• You have a strong publication record in a relevant scientific domain

Note that this is not an exhaustive or necessary list of requirements. Please consider applying if you believe you have the skills to contribute to Mistral's mission. We value profile and experience diversity.

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locationsNewmarket, Ontario

time typeFull time

posted onPosted 5 Days Ago

job requisition idJR102786

Position Overview:

Multimatic is seeking Automotive Paint Technicians who will be responsible for the meticulous preparation of carbon fibre and fiberglass bodywork and application of world-class primer/paint finishes.

This is a unique opportunity for candidates who have previous autobody paint experience and a passionate interest in automobiles.

Duties and Responsibilities:

• Must be organized and work in a safe manner, following all safety procedures

• Apply coatings to composite components using various spray methods

• Prepare components for coatings using various preparation methods

• Spot repair components using various spray methods

• Maintain shop tools and equipment including the spray facility

• Mix, spray and verify coatings to match

• Mask appropriate areas as required

• Inspect components during processing stages

• Follow work instructions and quality control processes

• Use body repair materials and methods for composite components

• Comfortable working in a dynamic team environment and providing/receiving constructive feedback

• Adhering to Multimatic’s quality-focused attitude and work culture

Training is provided as required.

Education and Experience:

• Experience in auto body finishing and repair

• Experience in spot repair using airbrush or spray gun

• Experience in colour matching

• Experience with painting two or three tone graphics across various panels

• Ability to interpret basic work instructions & follow mix ratios

• Ability to work with minimal supervision with high quality output

• An understanding of composite component construction is an asset

• Experience in manufacturing environments is an asset

This job posting is for an existing vacancy. The total cash compensation range for this position is expected to be:

$22.80 - $41.83

_To learn more about Multimatic, check out our youtube channel - https://www.youtube.com/watch?v=psOjJIh3t90_

_If you are interested in this position, apply by sending us your cover letter and resume._

_We thank all interested candidates in advance; however, only individuals selected for interviews will be contacted._

_As part of our commitment to ensuring our employment practices are fair, accessible, and inclusive of persons with disabilities, recruitment-related accommodations for disabilities, are available upon request throughout the recruitment and assessment process for applicants with disabilities._

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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.

Responsibilities:

We are seeking a Linux OS and System Programming Subject Matter Expert to join our Infrastructure team. In this role, you'll work on accelerating and optimising our virtualisation and VM workloads that power our AI infrastructure. Your expertise in low-level system programming, kernel optimisation, and virtualisation technologies will be crucial in ensuring Anthropic can scale our compute infrastructure efficiently and reliably for training and serving frontier AI models.

• Optimise our virtualisation stack, improving performance, reliability, and efficiency of our VM environments
• Design and implement kernel modules, drivers, and system-level components to enhance our compute infrastructure
• Investigate and resolve performance bottlenecks in virtualised environments
• Collaborate with cloud engineering teams to optimise interactions between our workloads and underlying hardware
• Develop tooling for monitoring and improving virtualisation performance
• Work with our ML engineers to understand their computational needs and optimise our systems accordingly
• Contribute to the design and implementation of our next-generation compute infrastructure
• Share knowledge with team members on low-level systems programming and Linux kernel internals
• Partner with cloud providers to influence hardware and platform features for AI workloads

You may be a good fit if you:

• Have experience with Linux kernel development, system programming, or related low-level software engineering
• Understand virtualisation technologies (KVM, Xen, QEMU, etc.) and their performance characteristics
• Have experience optimising system performance for compute-intensive workloads
• Are familiar with modern CPU architectures and memory systems
• Have strong C/C++ programming skills and ideally experience with systems languages like Rust
• Understand Linux resource management, scheduling, and memory management
• Have experience profiling and debugging system-level performance issues
• Are comfortable diving into unfamiliar codebases and technical domains
• Are results-oriented, with a bias towards practical solutions and measurable impact
• Care about the societal impacts of AI and are passionate about building safe, reliable systems

Strong candidates may also have experience with:

• GPU virtualisation and acceleration technologies
• Cloud infrastructure at scale (AWS, GCP)
• Container technologies and their underlying implementation (Docker, containerd, runc, OCI)
• eBPF programming and kernel tracing tools
• OS-level security hardening and isolation techniques
• Developing custom scheduling algorithms for specialised workloads
• Performance optimisation for ML/AI specific workloads
• Network stack optimisation and high-performance networking
• Experience with TPUs, custom ASICs, or other ML accelerators

Representative projects:

• Optimising kernel parameters and VM configurations to reduce inference latency for large language models
• Implementing custom memory management schemes for large-scale distributed training
• Developing specialised I/O schedulers to prioritise ML workloads
• Creating lightweight virtualisation solutions tailored for AI inference
• Building monitoring and instrumentation tools to identify system-level bottlenecks
• Enhancing communication between VMs for distributed training workloads

Deadline to apply:

None. Applications will be reviewed on a rolling basis.

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 the authenticity of an email or a request, please reach out to us directly.

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We are seeking an experienced Engineering Manager to lead the Cloud Inference team for AWS. You will lead your team to scale and optimize Claude to serve the massive audiences of developers and enterprise companies using AWS. You will own the end-to-end product of Claude on AWS, including API, load balancing, inference, capacity and operations. Your team will ensure our LLMs meet rigorous performance, safety and security standards and enhance our core infrastructure for packaging, testing, and deploying inference technology across the globe. Your work will increase the scale at which Anthropic operates and accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms.

Responsibilities:

• Set technical strategy and oversee development of Claude on AWS across all layers of the technical stack.
• Collaborate across teams and companies to deeply understand product, infrastructure, operations and capacity needs, identifying potential solutions to support frontier LLM serving
• Work closely with cross-functional stakeholders across companies to align on goals and drive outcomes
• Create clarity for the team and stakeholders in an ambiguous and evolving environment
• Take an inclusive approach to hiring and coaching top technical talent, and support a high performing team
• Design and run processes (e.g. postmortem review, incident response, on-call rotations) that help the team operate effectively and never fail the same way twice

You may be a good fit if you:

• Have 10+ years of experience in high-scale, high-reliability software development, particularly infrastructure or capacity management
• Have 5+ years of engineering management experience
• Experience recruiting, scaling, and retaining engineering talent in a high growth environment
• Have experience scaling products, resources and operations to accommodate rapid growth
• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
• Excel at building strong relationships and strategy with stakeholders across engineering, product, finance, and sales
• Have experience working with external partners to align goals and deliver impact
• Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space
• Have excellent written and verbal communication skills
• Demonstrated success building a culture of belonging and engineering excellence
• Are motivated by developing AI responsibly and safely
• Are willing and able to travel frequently between Seattle and the SF Bay Area

Strong candidates may also have experience with:

• Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Experience as a Product Manager
• Experience with deployment and capacity management automation
• Security and privacy best practice expertise

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 collaborative effort, and we work closely with other researchers, engineers, and experts to advance our understanding of AI and its applications.

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As a TPU Kernel Engineer, you'll be responsible for identifying and addressing performance issues across many different ML systems, including research, training, and inference. A significant portion of this work will involve designing and optimising kernels for the TPU. You will also provide feedback to researchers about how model changes impact performance.

You may be a good fit if you:

• Have significant experience optimising ML systems for TPUs, GPUs, or other accelerators
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• Designing and implementing kernels for TPUs or other ML accelerators
• Understanding accelerators at a deep level, e.g. a background in computer architecture
• ML framework internals
• Language modeling with transformers

Representative projects:

• Implement low-latency, high-throughput sampling for large language models
• Adapt existing models for low-precision inference
• Build quantitative models of system performance
• Design and implement custom collective communication algorithms
• Debug kernel performance at the assembly level

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 at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. 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.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process

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The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform—from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources—compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need engineers who can navigate these platform differences, build robust abstractions that work across providers, and make smart infrastructure decisions that keep us cost-effective at massive scale.

Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.

What You'll Do

• Design and build infrastructure that serves Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models
• Collaborate with CSP partner engineering teams to resolve operational issues, influence provider roadmaps, and stand up end-to-end serving on new cloud platforms
• Design and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions
• Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity
• Contribute to capacity planning and autoscaling strategies that dynamically match supply with demand across CSP validation and production workloads
• Optimize inference cost and performance across providers—designing workload placement and routing systems that direct requests to the most cost-effective accelerator and region
• Contribute to inference features that must work consistently across all platforms
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

You May Be a Good Fit If You:

• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code or container orchestration
• Have strong interest in inference
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and self-driven, taking ownership of problems end-to-end with a bias toward flexibility and high-impact work
• Pick up slack, even when it goes outside your job description

Strong Candidates May Also Have Experience With

• Direct experience working with CSP partner teams to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings
• A background in building platform-agnostic tooling or abstraction layers that work across cloud providers
• Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments
• Strong familiarity with LLM inference optimization, batching, caching, and serving strategies
• Experience with Machine learning infrastructure including GPUs, TPUs, Trainium, or other AI accelerators
• Background designing and building CI/CD systems that automate deployment and validation across cloud environments
• Solid understanding of multi-region deployments, geographic routing, and global traffic management
• Proficiency in Python or Rust

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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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 Infrastructure organisation 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.

The Systems engineering team owns compute uptime and resilience at massive scale, building the clusters, automation, and observability that make frontier AI research possible and safely deployable to customers.

_Team Matching: Team matching is determined after the interview process based on interview performance, interests, and business priorities. Please note we may also consider you for different Infrastructure teams._

Responsibilities

• Own the technical strategy and roadmap for your area, translating team-level goals into concrete execution plans
• Drive cross-team initiatives to build and scale AI clusters (thousands to hundreds of thousands of machines)
• Define infrastructure architecture, ensuring the hardest problems get solved — whether by you directly or by working through others
• Partner with cloud providers and internal stakeholders to shape long-term compute, data, and infrastructure strategy
• Establish and evolve operational excellence practices (incident response, postmortem culture, on-call)

You may be a good fit if you:

• Have 10+ years of software engineering experience
• Have led complex, multi-quarter technical initiatives that span multiple teams or systems
• Can set technical direction for a team, not just execute within it
• Have deep expertise in distributed systems, reliability, and cloud platforms (Kubernetes, IaC, AWS/GCP)
• Are strong in at least one systems language (Python, Rust, Go, Java)
• Naturally uplevel the engineers around you and can redirect efforts when things are heading off track
• Build alignment across senior stakeholders and communicate effectively at all levels

Strong candidates may have:

• Security and privacy best practice expertise
• Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Low level systems experience, for example linux kernel tuning and eBPF
• Technical expertise: Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systems

_Deadline to apply: None. Applications will be reviewed on a rolling basis._

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 at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This re

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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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As a Research Engineer on our team, you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines.

Responsibilities:

• Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
• Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
• Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI.
• Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
• Collaborate to translate experimental requirements into production-ready infrastructure
• Develop large scale data pipelines to handle advanced language model training requirements
• Optimize large scale training and inference pipelines for stable and efficient reinforcement learning

You may be a good fit if you:

• Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Are a strong communicator and enjoy working collaboratively
• Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Have proven track record of building large-scale data pipelines and distributed storage systems
• Excel at diagnosing and resolving complex infrastructure challenges in production environments
• Can work effectively across the full ML stack from data pipelines to performance optimization
• Have experience collaborating with other researchers to scale experimental ideas
• Thrive in fast-paced environments and can rapidly iterate from experimentation to production

Strong candidates may also have:

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
• Background in building infrastructure for AI research labs or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration.
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Comfort with large scale data pipelines (Beam, Spark, Dask, …)

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 projects, and we're committed to making a positive impact on the world.

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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 Infrastructure organisation 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.

The Systems engineering team owns compute uptime and resilience at massive scale, building the clusters, automation, and observability that make frontier AI research possible and safely deployable to customers.

_Team Matching: Team matching is determined after the interview process based on interview performance, interests, and business priorities. Please note we may also consider you for different Infrastructure teams._

Responsibilities

• Lead infrastructure projects from design through delivery, owning scope, execution, and outcomes
• Build and maintain systems that support AI clusters at massive scale (thousands to hundreds of thousands of machines)
• Partner with cloud providers and internal teams to solve compute, networking, and reliability challenges
• Tackle difficult technical problems in your domain and proactively fill gaps in tooling, documentation, and processes
• Contribute to operational practices including incident response, postmortems, and on-call rotations

You may be a good fit if you:

• Have 6+ years of software engineering experience
• Have led technical projects end-to-end over multiple months, including scoping, breaking down work, and driving delivery
• Have deep knowledge of distributed systems, reliability, and cloud platforms (Kubernetes, IaC, AWS/GCP)
• Are strong in at least one systems language (Python, Rust, Go, Java)
• Solve hard problems independently and know when to pull others in
• Help teammates grow through knowledge sharing and thoughtful technical guidance
• Communicate clearly in design docs, presentations, and cross-functional discussions

Strong candidates may have:

• Security and privacy best practice expertise
• Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Low level systems experience, for example linux kernel tuning and eBPF
• Technical expertise: Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systems

_Deadline to apply: None. Applications will be reviewed on a rolling basis._

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 at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic

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:

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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Location

Europe

Employment Type

Full time

Location Type

Remote

Department

EngineeringEngineering

Synthesia is the world's leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US.

As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations.

Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow.

About the role

• Lead the delivery of complex engineering projects, breaking them down into manageable stages with predictable timelines. Make informed decisions on trade-offs and effectively communicate these to stakeholders. Challenge and support senior engineers to achieve technical excellence.

• Understand engineering leveling and scale the team by identifying and filling skill gaps. Collaborate with recruiting to attract and retain top talent, ensuring the team structure supports current and future business needs.

• Monitor team performance proactively, identifying and addressing issues promptly. Develop strategies to retain top performers through growth opportunities and recognition.

• Navigate difficult conversations with clarity and empathy, especially when working with senior team members. Resolve disagreements by focusing on solutions that align with business goals and foster a collaborative team environment.

• Team you would be leading - Developer Platform is a team focused on helping engineers at Synthesia build, ship, and operate software faster and more safely by providing opinionated, self-service tooling with strong defaults. As the company grows, complexity grows with it. Our job is to create structure and tools to simplify and reduce that complexity so product teams don’t have to.

What we're looking for:

• Proven experience in leading engineering teams and delivering large-scale projects with predictable outcomes, ideally 2+ years.

• Strong understanding of engineering levels and experience in scaling teams effectively.

• Proven ability to track performance and helping every team member improve output and achieve career goals.

• Excellent communication skills, with experience in handling difficult conversations and resolving conflicts constructively.

• Relevant engineering background for a team building an enterprise-grade SaaS product.

• Experience in leading Platform/DevEx teams is a big plus, but not mandatory.

Why join us?

We’re living the golden age of AI. The next decade will yield the next iconic companies, and we dare to say we have what it takes to become one. Here’s why,

Our culture

At Synthesia we’re passionate about building, not talking, planning or politicising. We strive to hire the smartest, kindest and most unrelenting people and let them do their best work without distractions. Our work principles serve as our charter for how we make decisions, give feedback and structure our work to empower everyone to go as fast as possible. You can find out more about these principles here.

Serving 50,000+ customers (and 80% of the Fortune 500)

We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more. Read stories from happy customers and what 1,200+ people say on G2.

Proprietary AI technology

Since 2017, we’ve been pioneering advancements in Generative AI. Our AI technology is built in-house, by a team of world-class AI researchers and engineers. Learn more about our AI Research Lab and the team behind.

AI Safety, Ethics and Security

AI safety, ethics, and security are fundamental to our mission. While the full scope of Artificial Intelligence's impact on our society is still unfolding, our position is clear: People first. Always.  Learn more about our commitments to AI Ethics, Safety & Security.

The hiring process:

1. 30-40min call with a Technical Recruiter

1. 45min call with an Engineering Manager

1. Take-home assignment (no coding required) - writing an RFC, solution proposal

1. 60min task debrief and technical discussion

1. 45min call with leadership

The process does not need to take long - we can be done in seven working days.

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Location

San Francisco

Employment Type

Full time

Department

Scaling

Compensation

• $295K – $380K • 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.

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.

About the Team

The Kernels team at OpenAI builds the low-level software that accelerates our most ambitious AI research.

We work at the boundary of hardware and software, developing high-performance kernels, distributed system optimizations, and runtime improvements to make large-scale training and inference more efficient.

Our work enables OpenAI to push the limits by ensuring models - from LLMs to recommender systems - to run reliably on advanced supercomputing platforms. That includes adapting our software stack to new types of accelerators, tuning system performance end-to-end, and removing bottlenecks across every layer of the stack.

About the Role

On the Accelerators team, you will help OpenAI evaluate and bring up new compute platforms that can support large-scale AI training and inference.

Your work will range from prototyping system software on new accelerators to enabling performance optimizations across our AI workloads.

You’ll work across the stack, collaborating with both hardware and software aspects - working on kernels, sharding strategies, scaling across distributed systems, and performance modeling.

You'll help adapt OpenAI's software stack to non-traditional hardware and drive efficiency improvements in core AI workloads. This is not a compiler-focused role, rather bridging ML algorithms with system performance - especially at scale.

In this role, you will:

• Prototype and enable OpenAI's AI software stack on new, exploratory accelerator platforms.

• Optimize large-scale model performance (LLMs, recommender systems, distributed AI workloads) for diverse hardware environments.

• Develop kernels, sharding mechanisms, and system scaling strategies tailored to emerging accelerators.

• Collaborate on optimizations at the model code level (e.g. PyTorch) and below to enhance performance on non-traditional hardware.

Perform system-level performance modeling, debug bottlenecks, and drive end-to-end optimization.

• Work with hardware teams and vendors to evaluate alternatives to existing platforms and adapt the software stack to their architectures.

• Contribute to runtime improvements, compute/communication overlapping, and scaling efforts for frontier AI workloads.

You might thrive in this role if you have:

• 3+ years of experience working on AI infrastructure, including kernels, systems, or hardware-software co-design

• Hands-on experience with accelerator platforms for AI at data center scale (e.g., TPUs, custom silicon, exploratory architectures).

• Strong understanding of kernels, sharding, runtime systems, or distributed scaling techniques.

• Familiarity with optimizing LLMs, CNNs, or recommender models for hardware efficiency.

• Experience with performance modeling, system debugging, and software stack adaptation for novel architectures.

• Exposure to mobile accelerators is welcome, but experience enabling data center-scale AI hardware is preferred.

• Ability to operate across multiple levels of the stack, rapidly prototype solutions, and navigate ambiguity in early hardware bring-up phases

• Interest in shaping the future of AI compute through exploration of alternatives to mainstream accelerators.

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Microsoft AI are looking for a talented Principal Software Engineer at their Redmond office. This role sits at the heart of building the next generation platform for Bing and Microsoft AI. You'll work directly with stakeholders to determine requirements, lead the identification of dependencies and the development of design documents, and drive project plans, release plans, and work items.

About the Role

The Microsoft AI Web Data team is looking for a Principal Software Engineer to help us build the next generation platform for Bing and Microsoft AI. In Web Data, we are on a mission to build the most vast, safe, and accurate model of the Web to power search and AI. We are pushing frontiers of scalability and index quality by creating models and systems for discovering, storing, processing Web content, protecting our users & platform from Spam, Scams, and malware by keeping a step ahead of bad actors, and operating AI solutions.

Accountabilities

• Partner with stakeholders within Safety, Web Data, and partner teams, to determine requirements, lead the identification of dependencies and the development of design documents, and drive project plans, release plans, and work items.
• Lead by example, and mentor other engineers to produce extensible, scalable, high performance, resilient, and maintainable design and code.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Technical skills:

• Experience with processing Terabyte to Petabyte scale data with efficient algorithms for feature engineering, and experience with optimizing for high inference ROI and deploying AI/ML models including, but not limited to, Decision Tree and Forest models, encoder only and generative LLM/SLM models, multi-modal models, on NVIDIA, AMD, TPU or equivalent accelerators.

Personal attributes:

• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams.

Benefits

• Competitive salary and benefits package.
• Opportunities for professional growth and development.
• Collaborative and dynamic work environment.

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