You'll be part of our new Dynamic Pricing & Revenue Management team, working alongside a Data Scientist and a Data Analyst. Together, you will work towards one core goal: helping hosts improve occupancy and earnings through a smart, dynamic, and data-driven pricing strategy.

Our Tech Stack

• Data Storage & Querying: S3, Redshift (with decentralized data sharing), Athena, and DuckDB.
• ML & Model Serving: MLflow, SageMaker, and deployment APIs for model lifecycle management.
• Cloud & DevOps: Terraform, Docker, Jenkins, and AWS EKS (Kubernetes) for scalable, resilient systems.
• Monitoring: ELK, Grafana, Looker, OpsGenie, and in-house tools for full visibility.
• Ingestion: Kafka-based event systems and tools like Airbyte and Fivetran for smooth third-party integrations.
• Automation & AI: Extensive use of AI tools like Claude, Copilot, and Codex.

Your role in this journey

As a Data Ops Engineer – Revenue Management, you'll be the engineering backbone that enables our Data Scientists to move from experimentation to production. You bridge the gap between data science models and reliable, scalable production systems.

Responsibilities

• Support model deployment and serving: help deploy pricing and demand models into production, building and maintaining APIs and serving infrastructure.
• Build and operate production pipelines: ensure data flows reliably from source to model to output, with proper monitoring and alerting.
• Collaborate cross-functionally: work closely with Data Scientists, Analysts, and Engineering teams to turn prototypes into production-ready solutions.
• Own infrastructure and tooling: set up and maintain the environments, CI/CD pipelines, and infrastructure that the team depends on.
• Ensure operational excellence by implementing monitoring, automated testing, and observability across the team's production systems.
• Migrate and productionize POC: turn experimental code into robust, maintainable Python applications.
• Ensure data quality, consistency, and documentation across revenue management metrics and datasets.

Benefits

• Impact: Shape the future of travel with products used by millions of guests and thousands of hosts.
• Learning: Grow professionally in a culture that thrives on curiosity and feedback.
• Great People: Join a team of smart, motivated, and international colleagues who challenge and support each other.
• Technology: Work in a modern tech environment.
• Flexibility: Work a hybrid setup with 50% in-office time for collaboration, and spend up to 8 weeks a year from other inspiring locations.
• Perks on Top: Of course, we also offer travel benefits, gym discounts, and other perks to keep you energized.

Experience

• 4+ years of experience in Software Engineering, Data Engineering, DevOps, or MLOps.
• Strong hands-on skills in Python , you write clean, production-quality code.
• Experience with CI/CD, Docker, and infrastructure-as-code (e.g., Terraform).
• Familiarity with cloud platforms (AWS preferred) and deploying services in production.
• Exposure to or interest in ML model deployment (MLflow, SageMaker, or similar) is a strong plus.
• Desire to learn and use cutting-edge LLM tools and agents to improve your and the entire team's productivity.
• A proactive, hands-on mindset: you take ownership, spot problems, and drive solutions forward.

How to apply

If you're excited about this opportunity, please submit your application on our careers page!

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We're building the infrastructure that lets people talk to Claude,real-time, bidirectional voice conversations that feel natural, responsive, and safe. This is foundational work for how millions of people will interact with AI.

The Voice Platform team designs and operates the serving systems, streaming pipelines, and APIs that bring Anthropic's audio models from research into production across Claude.ai, our mobile apps, and the Anthropic API. You'll work at the intersection of real-time media, low-latency inference, and distributed systems,building infrastructure where every millisecond of latency is felt by the user.

We partner closely with the Audio research team, who train the speech understanding and generation models, and with product teams shipping voice experiences to users. Your job is to make those models fast, reliable, and delightful to talk to at scale.

Responsibilities

• Design and build the real-time streaming infrastructure that powers voice conversations with Claude,ingesting microphone audio, orchestrating model inference, and streaming synthesized speech back with minimal latency

• Build low-latency serving systems for speech models, optimizing time-to-first-audio and end-to-end conversational responsiveness

• Develop the public and internal APIs that expose voice capabilities to Claude.ai, mobile clients, and third-party developers

• Own the audio transport layer,codecs, jitter buffers, adaptive bitrate, packet loss recovery,so conversations stay smooth across unreliable networks

• Build observability and quality-measurement systems for voice: latency distributions, audio quality metrics, interruption handling, and turn-taking accuracy

• Partner with Audio research to move new model architectures from experiment to production, and feed real-world performance data back into research

• Collaborate with mobile and product engineering on client-side audio capture, playback, and the end-to-end user experience

You may be a good fit if you

• Have 6+ years of experience building distributed systems, real-time infrastructure, or platform services at scale

• Have shipped production systems where latency is measured in tens of milliseconds and users notice when you miss

• Are comfortable working across the stack,from transport protocols and serving infrastructure up to the APIs product teams build on

• Are results-oriented, with a bias toward flexibility and impact

• Pick up slack, even if it goes outside your job description

• Enjoy pair programming (we love to pair!)

• Care about the societal impacts of voice AI and want to help shape how these systems are developed responsibly

• Are comfortable with ambiguity,voice is a fast-moving space, and you'll help define the architecture as we learn what works

Strong candidates may also have experience with

• Real-time media protocols and stacks: WebRTC, RTP, gRPC bidirectional streaming, or WebSockets at scale

• Audio engineering fundamentals: codecs (Opus, AAC), voice activity detection, echo cancellation, jitter buffering, or audio DSP

• Low-latency ML inference serving, streaming model outputs, or GPU-based serving infrastructure

• Telephony, live streaming, video conferencing, or voice assistant platforms

• Mobile audio pipelines on iOS (AVAudioEngine, AudioUnits) or Android (Oboe, AAudio)

• Working alongside ML researchers to productionize models,speech experience is a plus but not required

Representative projects

• Driving time-to-first-audio below human perceptual thresholds by co-designing the serving pipeline with the Audio research team

• Building a streaming inference orchestrator that interleaves speech recognition, LLM reasoning, and speech synthesis with overlapping execution

• Designing the voice mode API surface for the Anthropic API so developers can build their own voice agents on Claude

• Implementing graceful barge-in and interruption handling so users can cut Claude off mid-sentence naturally

• Instrumenting end-to-end audio quality metrics and building dashboards that catch regressions before users do

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Your responsibilities will include:

• Owning and architecting large-scale data processing pipelines for robotics and autonomous vehicle datasets
• Building ML training and fine-tuning pipelines using Scale's robotics data
• Working across backend (Python, Node.js, C++) and frontend (React, TypeScript) stacks to build end-to-end solutions
• Developing tools and systems for robotics data collection, teleoperation, and model evaluation
• Interacting directly with robotics and AV stakeholders to understand their technical needs and drive product development
• Building real-time systems for robotic control, sensor fusion, and perception pipelines
• Designing comprehensive monitoring and evaluation frameworks for robotics models and data quality
• Collaborating with ML engineers and researchers to bring robotics research into production
• Delivering features at high velocity while maintaining system reliability and performance

Ideally, you have:

• 3+ years of software engineering experience in robotics, autonomous vehicles, or related fields
• Strong programming skills in Python and TypeScript/Node.js for production systems
• Experience with React and modern frontend development for 3D interfaces
• Practical experience with robotics frameworks (ROS/ROS2), simulation environments, or AV systems
• Understanding of distributed systems, workflow orchestration, and cloud infrastructure (AWS, Temporal, Kubernetes, Docker)
• Experience with databases (MongoDB, PostgreSQL) and data processing at scale
• Track record of working with cross-functional teams including ML engineers, researchers, and customers
• Strong communication skills and ability to operate with high autonomy

Nice to have:

• Experience with C++
• Experience with robotics hardware platforms (robotic arms, mobile robots, perception systems) with a focus on time synchronization
• Background in computer vision, SLAM, motion planning, or imitation learning
• Familiarity with autonomous vehicle data, lidar technologies, or 3D data processing
• Experience with ML model deployment and serving frameworks
• Knowledge of teleoperation systems (ALOHA, UMI, hand tracking) or VR interfaces
• Experience with workflow orchestration systems (Temporal, Airflow)
• Published research or open-source contributions in robotics or autonomous systems

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In this role, you'll interact directly with robotics and AV stakeholders to understand their technical needs and drive product development. You'll also design comprehensive monitoring and evaluation frameworks for robotics models and data quality, and collaborate with ML engineers and researchers to bring robotics research into production.

To succeed, you'll need at least 6 years of high-proficiency software engineering experience, with a strong background in complex systems and the ability to independently research, analyze, and unblock hard technical problems. You should have strong programming skills in Python and TypeScript/Node.js for production systems, experience with React and modern frontend development for 3D interfaces, and concurrent and real-time systems expertise.

We're looking for someone who can deliver features at high velocity while maintaining system reliability and performance, and has a track record of working with cross-functional teams including ML engineers, researchers, and customers. Strong communication skills and the ability to operate with high autonomy are essential.

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Your responsibilities will include:

• Owning and architecting large-scale data processing pipelines for robotics and autonomous vehicle datasets
• Building ML training and fine-tuning pipelines using Scale's robotics data
• Working across backend (Python, Node.js, C++) and frontend (React, TypeScript) stacks to build end-to-end solutions
• Developing tools and real-time systems for robotics data collection, teleoperation, model evaluation, data curation, and data annotation
• Interacting directly with robotics and AV stakeholders to understand their technical needs and drive product development
• Designing comprehensive monitoring and evaluation frameworks for robotics models and data quality

Ideal candidates will have:

• 3+ years of high-proficiency software engineering experience, with a strong background in complex systems and the ability to independently research, analyze, and unblock hard technical problems
• Strong programming skills in Python and TypeScript/Node.js for production systems
• Experience with React and modern frontend development for 3D interfaces
• Concurrent and real-time systems, with special attention to timing constraints
• Understanding of distributed systems, workflow orchestration, and cloud infrastructure (AWS, Temporal, Kubernetes, Docker)
• Experience with databases (MongoDB, PostgreSQL) and data processing at large scale
• Track record of working with cross-functional teams including ML engineers, researchers, and customers
• Strong communication skills and ability to operate with high autonomy

Nice to have:

• Experience with C++
• Experience with robotics hardware platforms (robotic arms, mobile robots, perception systems) with a focus on time synchronization
• Background in computer vision, SLAM, motion planning, or imitation learning
• Familiarity with autonomous vehicle data, lidar technologies, or 3D data processing
• Experience with ML model deployment and serving frameworks
• Knowledge of teleoperation systems (ALOHA, UMI, hand tracking) or VR interfaces
• Experience with workflow orchestration systems (Temporal, Airflow)
• Published research or open-source contributions in robotics or autonomous systems

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Key responsibilities include creating and driving engineering agendas to advance multimodal capabilities, improving data quality through annotation, filtering, augmentation, synthetic generation, captioning, and in-depth data studies, designing evaluation frameworks, metrics, benchmarks, evals, and reward models tailored to image/video/audio quality and coherence, implementing efficient algorithms for state-of-the-art model performance, and developing scalable data collection and processing pipelines for multimodal (primarily image/video-focused) datasets.

The ideal candidate will have a track record in leading studies that significantly improve neural network capabilities and performance through better data or modeling, experience in data-driven experiment designs, systematic analysis, and iterative model debugging, experience developing or working with large-scale distributed machine learning systems, and ability to deliver optimal end-to-end user experiences.

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Your responsibilities will include:

Architecting and owning the ML foundations for commerce discovery: user, item, and interaction embeddings that power personalized recommendations across shop surfaces (homepage, cart, post-purchase, wishlist, and more).

Designing and deploying scalable real-time recommendation and ranking systems that support a growing catalog of 1P and 3P items across heterogeneous game publisher inventories.

Building ML-powered marketing targeting systems that identify the right users for the right campaigns , new buyer discounts, drop campaigns, weekly deals, and seasonal promotions , driving conversion without conditioning users to wait for discounts.

Leveraging Discord's unique social graph to build social commerce ML: gifting recipient prediction, group buying conversion modeling, and friend-group recommendations that differentiate Discord from traditional game storefronts.

Driving deep learning A/B testing infrastructure and model monitoring to translate experimentation results into actionable product decisions.

Partnering closely with Shop, Game Commerce, Revenue Infra, ML Infra, and Data Engineering teams to define ML requirements, surface integration points, and influence the commerce roadmap.

To be successful in this role, you will need:

4+ years of experience as a Machine Learning Engineer, with a track record of owning and shipping recommendation or personalization systems end-to-end.

Deep expertise in applied deep learning , particularly embedding models, two-tower architectures, and retrieval/ranking systems for e-commerce or content recommendation.

Strong proficiency in Python and deep learning frameworks (PyTorch preferred).

Experience building and operating real-time ML serving infrastructure at scale, including feature stores, model serving, and A/B testing frameworks.

Demonstrated ability to work in early-stage, high-ambiguity environments and build ML systems from the ground up, not just improve existing ones.

Experience translating ML evaluation metrics and experiment results into product roadmap decisions and business impact.

Strong cross-functional instincts , you're comfortable partnering with product, engineering, data science, and business stakeholders to align on priorities and drive execution.

Bonus skills include experience applying graph ML or social network signals (social affinities, community behavior) to recommendation or personalization problems, familiarity with personalized marketing systems: lifecycle targeting, audience segmentation, and campaign optimization, and familiarity with loyalty, rewards, or incentive programs.

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Key responsibilities include:

• Leading, mentoring, and growing a high-performing engineering team responsible for both the customer-facing Model Serving product and its foundational infrastructure.
• Defining and owning the product and technical roadmap for Model Serving, balancing customer experience, functionality, and foundational investments across deployment, inference, monitoring, and scaling.
• Collaborating closely with product, research, platform, and infrastructure teams to drive end-to-end delivery from ideation and prioritization to launch and operation.
• Ensuring Model Serving meets stringent SLAs, SLOs, and performance and reliability goals, continuously improving operational efficiency and customer experience.
• Driving architectural decisions and product design around latency, throughput, autoscaling, GPU/CPU placement, and cost optimization.
• Advocating for customer needs through direct engagement, ensuring engineering decisions translate to clear product impact.
• Promoting best practices in code quality, testing, observability, and operational readiness.
• Fostering a culture of excellence, inclusion, and continuous improvement across the team.
• Partnering with recruiting to attract, hire, and develop top-tier engineering talent.

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Key Responsibilities:

• Serve as a technical lead on 8-10 multiple strategic opportunities, directly influencing the deal cycles and accelerating revenue growth.
• Become the definitive subject matter expert on Komodo's comprehensive suite of healthcare data assets and platform capabilities.
• Garner subject matter expertise and ownership of a segment within the Partnerships / Channel Partnerships organization.
• Develop scalable technical frameworks, demo environments, and reusable assets that have set new organizational standards with a heavy emphasis on agentic AI workflows.
• Drive cross-functional initiatives by partnering with Product, Data Science, and Engineering to deliver customized, innovative solutions.

Requirements:

• 7+ years of experience in Sales Engineering or Solutions Engineering with a focus on healthcare data and healthcare technology.
• Proven track record of understanding and leveraging AI tools to enhance SaaS products or improve operational workflows.
• Expertise in healthcare data (e.g., 837/835 transactions, NDC codes) and its practical applications in analytics, reporting, and decision-making.
• Strong technical skills, including experience with APIs, data integration, cloud-based architectures (e.g., AWS, Azure), and analyzing large datasets.
• An understanding and proficiency of data science techniques, specifically SQL, Python, and/or R.
• Excellent communication and presentation skills, with the ability to train partners and translate complex technical concepts for diverse stakeholders.

Preferred Skills:

• Experience working within the provider, payer, or financial service segments.
• Technical certifications in AWS, Azure, or data platforms.
• Experience with CRM platforms like Salesforce for managing partner and client interactions.
• Familiarity with data visualization tools (e.g., Tableau, Looker) to create impactful partner training materials.
• Knowledge of identity resolution and privacy-preserving linking technologies.
• Prior experience developing joint business plans and co-sell strategies with channel partners.

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This means driving deployments from scoping through production, running the platform work that spans them, and working across API, Platform Foundations, Security, our cloud provider counterparts, and whoever else is on the critical path when dependencies and tradeoffs pile up.

Responsibilities:

• Own end-to-end program execution for Anthropic’s API across major cloud deployments, from scoping through production launch and steady-state operations

• Drive the platform programs that cut across individual deployments: the shared foundations that get built once and reused, not rebuilt per cloud

• Act as a primary coordination point with cloud provider counterparts, keeping engagement clean across multiple internal teams with touchpoints into the same partner

• Partner with engineering leadership to turn technical direction into executable plans with clear owners, dependencies, and risk tracking

• Build the program scaffolding (roadmaps, status reporting, decision logs, escalation paths) that lets a fast-moving org stay aligned without slowing down

• Drive the hard sequencing conversations when partner commitments, engineering bandwidth, and priorities are in tension, and surface them to leadership with a recommendation

• Identify where program coverage is thin relative to the load and help shape how we staff around it

You may be a good fit if you:

• Have 10+ years of technical program management experience, including ownership of large infrastructure or platform programs with many engineering teams and external partners in the mix

• Have deep technical fluency in cloud APIs, infrastructure, distributed systems, or platform engineering, enough to be a credible partner to senior engineers on architecture and sequencing, not just a tracker of their decisions

• Have run programs spanning organizational boundaries where you had no direct authority over most of the people whose work you depended on, and delivered anyway

• Have direct experience with multi-cloud or hybrid cloud environments, large-scale migrations, or building platform abstraction layers

• Have worked with major cloud providers (AWS, GCP, Azure) or similar large technology partners, and know how to keep those relationships productive when priorities diverge

• Are comfortable operating in ambiguity on the long arc while being ruthlessly concrete on what ships this quarter and who owns it

• Have a track record of making a program get cheaper to run the second and third time, not just landing the first instance

• Thrive in environments where the plan you wrote last month needs rewriting, without losing the thread on what matters

Strong candidates may also:

• Have experience with production serving infrastructure, inference systems, or ML platform work

• Have moved between senior IC and management roles, or have interest in doing so

• Have worked at a company rebuilding systems and org in flight during rapid scale-up

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Responsibilities: Use proprietary tools to label and evaluate data. Support and ensure the delivery of high-quality curated data. Work with engineers to refine tasks, tools, and workflows. Design, select, and refine tasks grounded in real-world occupational math, for example: Insurance and risk: premiums, deductibles, expected loss calculations, simple risk modeling. Logistics and operations: inventory and reorder policies, capacity/throughput, basic cost optimization. Skilled trades: surveying and land measurement, electrical load calculations, machining tolerances, material estimation, blueprint and scale calculations. Finance and banking: loan amortization schedules, time value of money, portfolio and risk metrics. Accounting and tax: financial statements, reconciliations, depreciation, multi-bracket tax calculations, payroll. Health and social data: rates, ratios, simple biostatistics, survey-based metrics and policy-relevant indicators. Provide detailed, step-by-step solutions and evaluate model responses for correctness, adherence to domain rules (e.g., tax codes, building codes, basic regulatory or business constraints), clarity, and plausibility. Interpret, analyze, and execute tasks based on given instructions.

Basic Qualifications: A Master's or PhD in a quantitative field such as Mathematics, Statistics, Operations Research, Economics, Finance, Actuarial Science, or a closely related discipline with strong training in applied/occupational math; or A Bachelor's degree in one of the fields above plus substantial professional experience (e.g., 2+ years) in a math-heavy occupational domain such as finance, banking, insurance, accounting, logistics/supply chain, healthcare analytics, or policy analysis Professional licensure or certification in a skilled trade (e.g., licensed surveyor, master electrician, journeyman machinist) with demonstrated expertise in trade-specific mathematical calculations. Strong proficiency in applied mathematics relevant to at least one of the above domains (e.g., probability and statistics, financial math, optimization under constraints, geometric and measurement calculations, or similar). Proficiency in reading and writing, both in informal and professional English. Strong ability to navigate various information resources and databases. Outstanding communication, interpersonal, analytical, and organizational capabilities. Solid reading comprehension skills combined with the capacity to exercise autonomous judgment even when presented with limited data/material. A strong passion for and commitment to technological advancements and innovation.

Preferred Skills and Experience: Professional certifications in relevant fields (e.g., CFA, FRM, SOA exams, CPA, Six Sigma, PE, licensed surveyor, or similar) or demonstrably equivalent experience. Prior professional experience in one or more domains such as asset management, retail or commercial banking, insurance pricing/reserving, accounting/audit, logistics/supply chain planning, healthcare analytics, public policy analysis, or skilled trades. Previous AI Tutoring experience and/or experience teaching or training others in applied or occupational math topics. Experience building and reviewing complex spreadsheets, financial or risk models, dashboards, technical drawings, or similar artifacts.

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The Voice Platform team designs and operates the serving systems, streaming pipelines, and APIs that bring Anthropic's audio models from research into production across Claude.ai, our mobile apps, and the Anthropic API. You'll work at the intersection of real-time media, low-latency inference, and distributed systems,building infrastructure where every millisecond of latency is felt by the user.

We partner closely with the Audio research team, who train the speech understanding and generation models, and with product teams shipping voice experiences to users. Your job is to make those models fast, reliable, and delightful to talk to at scale.

Responsibilities:

• Design and build the real-time streaming infrastructure that powers voice conversations with Claude,ingesting microphone audio, orchestrating model inference, and streaming synthesized speech back with minimal latency

• Build low-latency serving systems for speech models, optimizing time-to-first-audio and end-to-end conversational responsiveness

• Develop the public and internal APIs that expose voice capabilities to Claude.ai, mobile clients, and third-party developers

• Own the audio transport layer,codecs, jitter buffers, adaptive bitrate, packet loss recovery,so conversations stay smooth across unreliable networks

• Build observability and quality-measurement systems for voice: latency distributions, audio quality metrics, interruption handling, and turn-taking accuracy

• Partner with Audio research to move new model architectures from experiment to production, and feed real-world performance data back into research

• Collaborate with mobile and product engineering on client-side audio capture, playback, and the end-to-end user experience

You may be a good fit if you

• Have 6+ years of experience building distributed systems, real-time infrastructure, or platform services at scale

• Have shipped production systems where latency is measured in tens of milliseconds and users notice when you miss

• Are comfortable working across the stack,from transport protocols and serving infrastructure up to the APIs product teams build on

• Are results-oriented, with a bias toward flexibility and impact

• Pick up slack, even if it goes outside your job description

• Enjoy pair programming (we love to pair!)

• Care about the societal impacts of voice AI and want to help shape how these systems are developed responsibly

• Are comfortable with ambiguity,voice is a fast-moving space, and you'll help define the architecture as we learn what works

Strong candidates may also have experience with

• Real-time media protocols and stacks: WebRTC, RTP, gRPC bidirectional streaming, or WebSockets at scale

• Audio engineering fundamentals: codecs (Opus, AAC), voice activity detection, echo cancellation, jitter buffering, or audio DSP

• Low-latency ML inference serving, streaming model outputs, or GPU-based serving infrastructure

• Telephony, live streaming, video conferencing, or voice assistant platforms

• Mobile audio pipelines on iOS (AVAudioEngine, AudioUnits) or Android (Oboe, AAudio)

• Working alongside ML researchers to productionize models,speech experience is a plus but not required

Representative projects

• Driving time-to-first-audio below human perceptual thresholds by co-designing the serving pipeline with the Audio research team

• Building a streaming inference orchestrator that interleaves speech recognition, LLM reasoning, and speech synthesis with overlapping execution

• Designing the voice mode API surface for the Anthropic API so developers can build their own voice agents on Claude

• Implementing graceful barge-in and interruption handling so users can cut Claude off mid-sentence naturally

• Instrumenting end-to-end audio quality metrics and building dashboards that catch regressions before users do

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This means driving deployments from scoping through production, running the platform work that spans them, and working across API, Platform Foundations, Security, our cloud provider counterparts, and whoever else is on the critical path when dependencies and tradeoffs pile up.

Responsibilities:

• Own end-to-end program execution for Anthropic’s API across major cloud deployments, from scoping through production launch and steady-state operations

• Drive the platform programs that cut across individual deployments: the shared foundations that get built once and reused, not rebuilt per cloud

• Act as a primary coordination point with cloud provider counterparts, keeping engagement clean across multiple internal teams with touchpoints into the same partner

• Partner with engineering leadership to turn technical direction into executable plans with clear owners, dependencies, and risk tracking

• Build the program scaffolding (roadmaps, status reporting, decision logs, escalation paths) that lets a fast-moving org stay aligned without slowing down

• Drive the hard sequencing conversations when partner commitments, engineering bandwidth, and priorities are in tension, and surface them to leadership with a recommendation

• Identify where program coverage is thin relative to the load and help shape how we staff around it

You may be a good fit if you:

• Have 10+ years of technical program management experience, including ownership of large infrastructure or platform programs with many engineering teams and external partners in the mix

• Have deep technical fluency in cloud APIs, infrastructure, distributed systems, or platform engineering, enough to be a credible partner to senior engineers on architecture and sequencing, not just a tracker of their decisions

• Have run programs spanning organizational boundaries where you had no direct authority over most of the people whose work you depended on, and delivered anyway

• Have direct experience with multi-cloud or hybrid cloud environments, large-scale migrations, or building platform abstraction layers

• Have worked with major cloud providers (AWS, GCP, Azure) or similar large technology partners, and know how to keep those relationships productive when priorities diverge

• Are comfortable operating in ambiguity on the long arc while being ruthlessly concrete on what ships this quarter and who owns it

• Have a track record of making a program get cheaper to run the second and third time, not just landing the first instance

• Thrive in environments where the plan you wrote last month needs rewriting, without losing the thread on what matters

Strong candidates may also:

• Have experience with production serving infrastructure, inference systems, or ML platform work

• Have moved between senior IC and management roles, or have interest in doing so

• Have worked at a company rebuilding systems and org in flight during rapid scale-up

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You will work closely with teams across Anthropic to improve reliability across our most critical serving paths. You will be responsible for making the systems that deliver Claude more robust and resilient, whether during an incident or collaborating on projects.

To be successful in this role, you should have strong distributed systems, infrastructure, or reliability backgrounds. You should be curious and brave, comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.

You will be working on high-availability serving infrastructure across multiple regions and cloud providers. You will support the reliability of safeguard model serving, which is critical for both site reliability and Anthropic's safety commitments.

If you're committed to creating reliable, interpretable, and steerable AI systems, and you're passionate about working on complex technical problems, we'd love to hear from you.

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As the engineer responsible for Safeguards on those surfaces, you will ensure that every request served through our CSP partners is monitored for misuse, enforced against policy, and compliant with the data residency and privacy commitments that enterprise CSP customers expect.

You will sit at the seam between the Safeguards organisation and the Cloud Inference team: taking classifiers, detection signals, and enforcement policies developed by Safeguards and making them run reliably inside a CSP partner's infrastructure at serving-path latency and scale.

Responsibilities:

• Build, deploy and operate real-time safeguards infrastructure,classifiers, rate limits, enforcement actions, and intervention hooks,embedded directly in the third-party CSP inference serving path

• Design and maintain the data residency and privacy architecture for safeguards signals on CSP platforms, ensuring we can detect abuse and monitor model behaviour while honouring regionalisation boundaries and enterprise contractual commitments

• Develop telemetry, logging, and evaluation pipelines that give Safeguards, Policy, and T&S operational teams situational awareness over CSP traffic and close the visibility gap between third-party and first-party serving

• Dive into the CSP serving stack to identify the lowest-impact points to gather signals or introduce interventions without degrading latency, stability, or overall architecture

• Hold a high operational bar: own on-call, drive root-cause analyses and postmortems for safeguards incidents on CSP platforms, and build systems that reduce the human intervention required to keep Claude safe

• Work closely with Safeguards research, Policy & Enforcement, the Cloud Inference team, and CSP partner contacts to turn detection research and policy decisions into production enforcement that works inside a partner's cloud.

You may be a good fit if you:

• Have a Bachelor's degree in Computer Science, Software Engineering, or comparable experience

• Have 4–10+ years of experience in high-scale, high-reliability software development, ideally with exposure to trust & safety, anti-abuse, fraud, or integrity systems

• Are proficient in Python and comfortable working across the stack,from request-path services to data pipelines to internal tooling

• Think adversarially: you can see a system from a bad actor's perspective, anticipate how they will respond to countermeasures, and design defences in depth rather than single points of enforcement

• Have experience scaling infrastructure to accommodate rapid traffic growth while keeping latency and reliability within tight budgets

• Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development

• Have strong communication skills and can explain complex technical and risk tradeoffs to non-technical stakeholders across Policy, Legal, and partner organisations

• Enjoy working in a fast-paced, early environment; comfortable with adapting priorities as driven by the rapidly evolving AI space

Strong candidates may also have experience with:

• Building trust and safety, anti-spam, fraud, or abuse detection and mitigation mechanisms for AI/ML systems, or the infrastructure to support these systems at scale

• Machine learning serving infrastructure (GPUs/TPUs, inference servers, load balancing) and the operational realities of running models in production

• Major cloud platform internals,IAM, Network/service perimeter controls, regional resource constraints, cloud-native logging/monitoring,or experience shipping software that runs inside a partner's cloud rather than your own

• Data residency, privacy engineering, or compliance-constrained architectures, particularly where telemetry has to stay within regional or contractual boundaries

• Working closely with operational and human-review teams to build custom internal tooling, admin UX, and alerting

• Adversarial mindset: has shipped defences against motivated attackers before, knows what it feels like when they adapt, and can sprint to close a gap before it becomes an incident

• Comfortable operating at the intersection of platform/infra engineering and trust & safety,neither a pure infra engineer nor a pure T&S engineer, but someone who can credibly do both

• Has shipped software that runs inside someone else's infrastructure (partner cloud, embedded deployment, or similar) and knows how to get things done when you don't control the whole stack

• Senior enough to own a cross-team seam independently, drive consensus across orgs, and make latency/safety tradeoff calls without escalation

• TypeScript or Rust, and agentic coding tools such as Claude Code

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role's On Target Earnings ('OTE') range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $405,000-$485,000 USD

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In this role, you'll lead the Spanish sales team in driving revenue growth by partnering with agencies, direct clients, and managing a team of high-performing sales people in Spain.

If you're an enthusiastic X user with relevant experience in digital advertising, people leadership, agency partnerships, and a proven track record of building strong customer relationships, we invite you to explore this opportunity to contribute to X's advertising sales and agency development.

Responsibilities

• Generate revenue growth for X by managing industry sales across key verticals and agency partnerships, via direct client and agency relationships.
• Lead, manage, and inspire a high-performing sales team, including Client Partners, Client Account Managers, and agency-focused team members, to consistently deliver strong sales results and meet quarterly targets.
• Actively coach and develop the team on core sales competencies, while partnering with Market Leads, Global Agency Team, Revenue Finance, Sales, and cross-functional (XFN) teams to build and execute cohesive strategy for the Spanish Market.
• Own quarterly regional sales forecasts, planning, and execution to drive incremental revenue.
• Build trusted partnerships, expand and deepen relationships, and facilitate active communication between agencies, clients, and X, including developing strategic relationships across all agency types (creative, media, social, etc.) to deliver optimal value through trading agreements and program execution.
• Use in-depth industry, vertical, and agency knowledge to build winning strategies, remove blockers, maximize revenue, and provide thought leadership in the market and key sectors.
• Develop and execute trading strategies for local collaborative agreements that drive revenue for X and value for agencies and clients.
• Develop and deliver educational bootcamp-style training on X products, brand safety tools, best practices, and how individuals use X to inspire authentic brand applications.
• Ensure agencies' and clients' voices, opinions, ideas, and feedback are conveyed back to X's Marketing, Product Development, and wider business teams, including input on sales materials, advertiser insights, and new products/features.
• Partner with sales teams on support plans, growth strategies, and day-to-day matters to increase revenue from existing accounts, unlock new advertisers, and develop first-class programs to uplevel creative thinking and deliver positive business impact.
• Discover and showcase great X usage stories and evidence to shape the agency, brand, and media world's understanding of how brands can use X.
• Build and maintain strong relationships with all key stakeholders across clients and agencies.
• Report on revenue, understand macro trends and internal forces impacting the business, and provide these insights back to the wider organization.
• Provide deep vertical, product knowledge, and insights to drive creative and long-term improvements to X's overall platform offering.

Basic Qualifications

• A deep understanding of both performance and brand marketing, as well as the wider online advertising industry, with a focus on tools and technologies that enable ad serving.
• 15+ years of proven experience in digital media sales, marketing, agency partnerships, or media sales, with specific expertise across the Spanish market and EMEA verticals.
• Demonstrated ability to think strategically about industries, product sets, and complex agency structures.
• Deep expertise in social media platforms, including demonstrated experience in X best practices.
• Strong client-facing, sales, and commercial skills, with experience in partner/agency management, business development, negotiations, and direct advertiser sales, including strategic partnerships.
• Familiarity with agency holding companies and strong relationships across the agency landscape.
• Experience successfully working with Sales XFN teams, Product, Engineering, and Marketing groups to develop comprehensive forward-looking plans and execute them with strategic and tactical initiatives.
• Track record in building, managing, and coaching a high-performing sales team, as well as building senior client and agency relationships.
• Ability to partner within cross-functional teams, consult, bring ideas to the table, and build internal networks for collaboration across teams.
• Strong writing, copy editing, communication, and presentation skills, including proficiency in PowerPoint and Keynote.
• A self-starter who takes initiative, is action-oriented, and can balance quick turnaround with long-term strategic efforts; flexible and effective in dealing with changes in priorities, ambiguity, or obstacles.
• Comfortable with a fast-paced, always-on, start-up environment.
• Team player with a positive and dynamic personality.
• Strong planning and organizational capabilities.
• Alignment to X’s vision and core values.

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You may be a good fit for this role if you have strong distributed systems, infrastructure, or reliability backgrounds, are curious and brave, think holistically about how systems compose and where the seams are, can build lasting relationships across teams, care about users and feel ownership over outcomes, have excellent communication and collaboration skills, and bring diverse experience.

Strong candidates may also have experience operating large-scale model serving or training infrastructure, experience with one or more ML hardware accelerators, understanding of ML-specific networking optimizations, expertise in AI-specific observability tools and frameworks, experience with chaos engineering and systematic resilience testing, and contributions to open-source infrastructure or ML tooling.

We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. We value impact and believe that the highest-impact AI research will be big science. We work as a single cohesive team on just a few large-scale research efforts and value communication skills.

If you're interested in this role, please submit an application even if you don't believe you meet every single qualification. We encourage diversity and strive to include a range of diverse perspectives on our team.

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Key responsibilities include:

• Architecting the Gemini app serving and orchestration layers, writing design docs, and defining interfaces to ensure the codebase is scalable, modular, and capable of supporting rapid innovation.
• Designing and implementing robust CI/CD pipelines and experimentation platforms, building tooling that enables the wider engineering team to utilize A/B testing and feature flags to safely and quickly iterate.
• Driving application performance initiatives, debugging complex production issues, and advocating for code quality standards to ensure the infrastructure scales to our product needs.
• Acting as the strategic technical counterpart to product and design leadership, assessing feasibility of ambitious concepts, and proposing technical solutions that turn AI capabilities into reality.
• Mentoring staff and senior engineers, leading code reviews, and fostering a culture of technical accuracy, psychological safety, and user-centricity.

In order to set you up for success, we look for the following skills and experience:

• Bachelor's degree in Computer Science or Engineering, or equivalent practical experience.
• 15 years of experience in software engineering, building and working with systems in the technology organization.

In addition, the following would be an advantage:

• Experience building large-scale serving infrastructure.
• Experience implementing observability, telemetry, and real-time monitoring strategies.
• Ability to design and refactor complex server-side architectures that have scaled, ideally at the >1 billion user scale.
• Ability to analyze data to identify bottlenecks and drive technical decisions regarding performance optimizations.
• Ability to unblock teams by solving the hardest technical problems, balancing technical debt with feature work, and driving predictable delivery through architectural clarity.
• Ability to drive technical consensus across multiple teams and stakeholders, translating technical constraints into clear options for leadership.

The US base salary range for this full-time position is between $307,000 - $427,000 + bonus + equity + benefits.

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You'll shape the future of fal's inference engine and ensure our generative models achieve best-in-class performance. Your work directly impacts our ability to rapidly deliver cutting-edge creative solutions to users, from individual creators to global brands.

Day-to-day, you'll set technical direction, guide your team to build high-performance inference solutions, and personally contribute to critical inference performance enhancements and optimizations. You'll collaborate closely with research & applied ML teams, influence model inference strategies and deployment techniques, and drive advanced performance optimizations.

As a leader, you'll mentor and scale your team, coach and expand your team of performance-focused engineers, and help them innovate, solve complex performance challenges, and level up their skills.

To succeed in this role, you'll need to be deeply experienced in ML performance optimization, understand the full ML performance stack, and know inference inside-out. You'll also need to thrive in cross-functional collaboration and have excellent leadership skills.

If you're ready to lead the future of inference performance at a fast-paced, high-growth frontier, apply now!

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We are seeking a highly skilled and experienced Staff Software Engineer to join our Communication Products team. As a Staff Engineer, you will be responsible for leading the technical vision for ML-powered messaging features, architecting and delivering intelligent capabilities end-to-end, and partnering deeply with ML and product teams.

The Difference You Will Make:

As a Staff Engineer on the team, you will define and drive the technical strategy for integrating ML capabilities into Airbnb's messaging products, including smart replies, message classification, content moderation, translation, and conversational assistance. You will also own the full lifecycle of ML-powered features: from prototyping and experimentation through launch, monitoring, and iteration.

A Typical Day:

• Design, build, and operate the systems that serve ML models within the messaging stack, with a focus on latency, reliability, and scalability
• Write and review technical designs that solve large, open-ended problems at the intersection of ML and product engineering without clearly-known solutions
• Partner with ML, data science, and product teams to identify high-value opportunities, establish evaluation criteria, and close the gap between offline model performance and production impact
• Collaborate with other engineers and cross-functional partners across Messaging, Trust & Safety, Localization, and Platform organizations to align on long-term technical solutions
• Mentor, guide, advocate, and support the career growth of individual contributors
• Establish engineering standards for ML integration across the messaging surface, including feature flagging, A/B testing, observability, and graceful degradation

Your Expertise:

• 9+ years of relevant engineering hands-on work experience
• Bachelors, Masters, or PhD in CS or related field
• Demonstrated experience building and shipping ML-powered product features in production environments, including model serving, feature pipelines, online/offline evaluation, and monitoring
• Exceptional architecture abilities and experience with architectural patterns of large, high-scale applications
• Familiarity with NLP/NLU techniques and large language models, particularly as applied to messaging, conversational AI, or content understanding
• Shipped several large-scale projects with multiple dependencies across teams, specifically at the intersection of ML infrastructure and product engineering
• Technical leadership and strong communication skills with the ability to translate between ML research, product goals, and engineering execution
• Experience operating distributed, real-time systems at scale with high reliability requirements
• Experience with real-time messaging systems or event-driven architectures
• Familiarity with ML infrastructure at scale (e.g., feature stores, model registries, online inference platforms)
• Prior work on trust & safety, content moderation, or internationalization in a messaging context
• Experience with LLM-based product features, including prompt engineering, retrieval-augmented generation, or fine-tuning

How We'll Take Care of You:

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range: $204,000-$255,000 USD

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You will own and shape the architecture of Reddit's Android Mobile App that will scale us to the next 100M+ DAUs. Propose ideas/solutions to make Android at Reddit best-in-class.

Improve Android developer experience by designing tools, workflows, and CI integrations that make it fast and safe to develop, test, and release code.

Define and operationalize guardrails (lint/static analysis, tests, and AI-assisted reviews) that catch common issues early.

Build and evolve Android client health and observability foundations (events, traces, dashboards) so teams can improve user experiences.

Apply AI thoughtfully to engineering workflows (e.g., code review, static analysis, CI checks) for improved developer productivity and/or user experiences.

Mentor and Support Android engineers through design reviews, documentation, and education on platform capabilities, observability, and best practices.

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We're looking for engineers who have owned high scale operational sensitive systems like customer facing APIs, Edge Gateways, ML Inference, or similar services and have an interest in getting deep building LLM APIs and runtimes at scale. As a Staff Engineer, you'll play a critical role in shaping both the product experience and core infrastructure.

The impact you will have:

• Design and implement core systems and APIs that power Databricks Foundation Model Serving, ensuring scalability, reliability, and operational excellence.
• Partner with product and engineering leadership to define the technical roadmap and long-term architecture for serving workloads.
• Drive architectural decisions and trade-offs to optimize performance, throughput, autoscaling, and operational efficiency for GPU serving workloads.
• Contribute directly to key components across the serving infrastructure , from working in systems like vLLM and SGLang to creating token based rate limiters and optimizers , ensuring smooth and efficient operations at scale.
• Collaborate cross-functionally with product, platform, and research teams to translate customer needs into reliable and performant systems.
• Establish best practices for code quality, testing, and operational readiness, and mentor other engineers through design reviews and technical guidance.
• Represent the team in cross-organizational technical discussions and influence Databricks’ broader AI platform strategy.

What we look for:

• 10+ years of experience building and operating large-scale distributed systems.
• Experience leading high-scale operationally sensitive backend systems.
• A track record of up-leveling teams engineering excellence.
• Strong foundation in algorithms, data structures, and system design as applied to large-scale, low-latency serving systems.
• Proven ability to deliver technically complex, high-impact initiatives that create measurable customer or business value.
• Strong communication skills and ability to collaborate across teams in fast-moving environments.
• Strategic and product-oriented mindset with the ability to align technical execution with long-term vision.
• Passion for mentoring, growing engineers, and fostering technical excellence.

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As part of the Safeguards team, you'll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable.

Responsibilities:

• Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
• Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications
• Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
• Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
• Implement automated testing, deployment, and rollback systems for ML models in production safety applications
• Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

You may be a good fit if you:

• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
• Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
• Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Care deeply about AI safety and the societal impacts of your work

Strong candidates may have experience with:

• Working with large language models and modern transformer architectures
• Implementing A/B testing frameworks and experimentation infrastructure for ML systems
• Developing monitoring and alerting systems for ML model performance and data drift
• Building automated labeling systems and human-in-the-loop workflows
• Experience in trust & safety, fraud prevention, or content moderation domains
• Knowledge of privacy-preserving ML techniques and compliance requirements
• Contributing to open-source ML infrastructure projects

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.

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You will join a team that ships products used by thousands of the world's most sophisticated data and AI organizations. You will drive the vision and roadmap for AI platform product areas and define how customers build, train, deploy, and monitor AI and ML systems on Databricks. You will collaborate across engineering teams to deliver an integrated and powerful path from experimentation to production.

The impact you will have:

• Own the product roadmap for AI platform areas , defining what we build, why, and in what order , to accelerate customer adoption of AI and ML in production.
• Drive strategy for key AI platform capabilities, shaping how enterprises operationalize AI at scale.
• Partner closely with engineering teams to make deeply technical decisions about ML infrastructure , from distributed training architectures to real-time serving systems.
• Represent the voice of the customer by engaging directly with enterprise ML teams, translating their pain points and workflows into platform capabilities that simplify the path to production AI.
• Collaborate with GTM, Solutions Architecture, and Customer Success teams to drive enterprise adoption, shape field enablement, and inform competitive positioning.
• Define pricing, packaging, and commercialization strategy for AI platform features, working with business teams to maximize value capture.
• Grow end-user engagement with Databricks AI tools by identifying adoption bottlenecks and partnering cross-functionally to remove them.

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As a senior machine learning engineer, you will play a key role in the end-to-end design and implementation of our product, which is a platform for powering use cases across training and serving of generative AI models. You will work closely with both customers and internal ML researchers to identify key areas of development for our generative AI platform.

Key responsibilities include:

• Designing and building the core platform infrastructure that supports our customer-facing product features
• Ensuring the reliability, security, and scalability of the backend distributed systems that power all aspects of our product
• Translating product requirements into user interfaces and backend distributed system design and owning end-to-end implementation

We look for:

• 4+ years of hands-on programming experience with at least one modern language such as Python, Scala, Go, or C++
• Strong sense of distributed systems design and experience building large-scale systems
• Experience building ML platform systems for applications in the ML model development lifecycle such as data preparation, model training, model evaluation, and model serving
• Direct experience developing ML models is a plus but not required
• Strong sense of end-to-end product ownership as well as intuition for both robust system design and product usability
• Effective communication skills and the ability to articulate complex technical ideas to cross-disciplinary internal and external stakeholders

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.

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The Impact You Will Have:

• Build: You will design and develop robust frontend interfaces and API backends (e.g., FastAPI routing user queries to model-serving endpoints). You will build solutions ranging from data-rich dashboards to enterprise chat solutions powered by the Mosaic AI Agent Framework.

• Architect: You will design secure and scalable application architectures that can suffice GTM requirements on building custom SaaS applications.

• Scale: You will create scalable applications that seamlessly connect to Databricks SQL via the Statement Execution API or Databricks SDK. You will establish CI/CD pipelines using Declarative Automation Bundles (DABs) to automate deployment across development, staging, and production workspaces.

What we look for:

• You have 5+ years of experience working as a Software Engineer, Data App Developer, or Full-Stack Engineer building interactive web applications.

• You are proficient in Python, DBSQL and/or Node.js. Experience with frameworks like Streamlit, Dash, Flask, FastAPI, React, or Express is required.

• You know the Databricks ecosystem. Familiarity with Unity Catalog, Databricks SQL, Databricks SDK for Python, and Model Serving is highly preferred.

• You have built for scale and security. Experience with CI/CD tools, Infrastructure as Code (specifically Databricks Asset Bundles), and implementing secure OAuth flows.

• You are passionate about applying AI. Experience integrating LLMs or Mosaic AI Agent Frameworks into application backends to deliver intelligent chat and RAG solutions.

• You excel in a collaborative environment. You can translate stakeholder requirements into intuitive user interfaces, working through dependencies and troubleshooting deployment errors or telemetry logs.

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

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Our mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence.

As a Member of Technical Staff - Inference, you will be responsible for optimising the latency and throughput of model inference, building reliable and performant production serving systems to serve billions of users, accelerating research on scaling test-time compute and rollout in reinforcement learning training, and model-hardware co-design for next-generation architectures.

To be successful in this role, you will need to have worked on system optimisations for model serving, such as batching, caching, load balancing, and parallelism, worked on low-level optimisations for inference, such as GPU kernels and code generation, worked on algorithmic optimisations for inference, such as quantisation, distillation, and speculative decoding, and low-precision numerics, worked on large-scale inference engines or reinforcement learning frameworks, worked on large-scale, high-concurrent production serving, and worked on testing, benchmarking, and reliability of inference services.

The base salary for this role is $180,000 - $440,000 USD, and we offer a comprehensive total rewards package, including equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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The Imagine Product team is redefining AI-driven media experiences for Grok users worldwide. You'll build and scale robust, high-performance systems that power immersive, multi-modal media interactions,leveraging cutting-edge AI to enable seamless generation, processing, and delivery of images, video, audio, and beyond.

Your work will drive engaging, real-time user experiences that captivate and delight millions, turning advanced multimodal models into production-grade features. If you're a driven problem-solver passionate about AI, media technologies, and creating scalable solutions that shape the future of consumer AI, this is your opportunity to make a lasting impact.

Responsibilities

• Design and implement scalable systems to support Grok's AI-driven media experiences, ensuring high performance, reliability, and low-latency at global scale.
• Architect robust infrastructure for real-time multi-modal interactions, including handling generation requests, media processing, and seamless integration with frontend and model serving layers.
• Build and optimise large-scale data pipelines to ingest, process, and analyse multi-modal data (images, video, audio), fueling continuous improvement and personalisation of Grok's media capabilities.
• Collaborate closely with frontend engineers, AI researchers, and product teams to deliver captivating, media-rich features and end-to-end user experiences.
• Own full-cycle development of solutions: from system design and prototyping to deployment, monitoring, observability, and iterative refinement.
• Deliver production-ready, maintainable code that powers features reaching hundreds of millions of users.

Basic Qualifications

• Proficiency in Python or Rust, with a strong track record of writing clean, efficient, maintainable, and scalable code.
• Experience designing and building systems for consumer-facing products, with emphasis on performance, reliability, and handling high-throughput workloads.
• Hands-on expertise in large-scale data infrastructure and pipelines, particularly for multi-modal or media-heavy AI applications.
• Proven ability to deliver robust, production-grade solutions to millions of users while maintaining high standards of quality and uptime.
• Strong problem-solving skills and a passion for turning innovative ideas into high-impact, scalable realities.
• Deep enthusiasm for AI and media technologies, with a commitment to building user-focused products that inspire and engage.

Preferred Skills and Experience

• Experience with real-time systems, inference serving, or multi-modal data processing at scale.
• Familiarity with distributed systems, containerisation (e.g., Kubernetes), observability tools, or performance tuning for AI workloads.
• Background in AI-driven consumer products or media generation technologies.
• Track record collaborating across engineering, research, and product teams to ship delightful features quickly.

Compensation and Benefits

$180,000 - $440,000 USD

Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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This role is pivotal in developing foundational multimodal AI capabilities to understand, generate, and protect human likeness. As a key contributor, you will design and implement cutting-edge models and frameworks, pushing the boundaries of AI to enable foundational capabilities for human-centric understanding and generation.

This is a unique opportunity to contribute to impactful research and advance Google DeepMind's mission towards Artificial General Intelligence (AGI).

Key Responsibilities

• Advance multimodal human representations & understanding: Research and implement novel models and other multimodal techniques for a more holistic understanding of humans across visual, audio, and textual data.
• Conduct applied research: Conduct experimental research cycles from hypothesis to deployment.
• Drive technical projects: Take ownership of substantial technical projects within the effort, from ideation and design to implementation and evaluation, often involving cross-functional collaboration.
• Contribute to Infrastructure: Inform and contribute to the development of scalable and efficient research infrastructure for multimodal human understanding models and datasets.
• Design and execute strategies for tuning and adapting VLMs and other foundation models for specific tasks

Requirements

• PhD degree in Computer Science, Machine Learning, or a related technical field with 3+ years of relevant experience.
• Experience in developing machine learning models, such as audio & speech-visual models.
• Experience in working with and tuning large-scale vision language models.
• Strong programming skills in Python and experience with at least one major deep learning framework (e.g., JAX)
• Experience conducting independent research and development, including experimental design, implementation, and analysis.

Salary

The US base salary range for this full-time position is between $174,000 USD - $252,000 USD + bonus + equity + benefits.

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Responsibilities: Conduct in-depth code reviews and static analysis to identify and mitigate security vulnerabilities in our applications Design and implement secure coding guidelines and best practices for development teams Collaborate closely with development teams to integrate security practices throughout the CI/CD pipeline Perform threat modeling and risk assessments for applications, developing mitigation strategies for potential risks Manage vulnerability tracking and remediation efforts, providing guidance to development teams Support incident response activities related to application security Stay current on emerging security threats and trends in cloud-native technologies and AI, continuously enhancing our security measures Evaluate and secure software supply chains, including producing and maintaining Software Bills of Materials (SBOMs) Address security concerns specific to AI and machine learning models, with a focus on the OWASP LLM Top 10

Basic Qualifications: Bachelor's degree in Computer Science, Cybersecurity, or a related field 3-5 years of experience in application security, with a strong focus on code security practices Deep understanding of secure coding practices, application security frameworks, and common vulnerabilities (e.g., OWASP Top 10) Proficiency in Python or Rust programming languages and experience with secure coding practices in these languages Experience securing CI/CD pipelines and implementing DevSecOps practices Familiarity with software supply chain security and SBOM generation tools Experience with security testing tools (e.g., Burp Suite, OWASP ZAP) and static/dynamic code analysis Understanding of AI/ML security implications, particularly those outlined in the OWASP LLM Top 10 Excellent communication skills, able to explain complex security issues to both technical and non-technical audiences

Preferred Skills and Experience: Experience with cloud platforms (e.g., GCP, AWS, Azure) and their security features Relevant security certifications (e.g., CSSLP, OSWE) Background in data privacy and compliance regulations relevant to cloud-native applications and AI systems Experience with GitOps and infrastructure-as-code security Familiarity with federated learning and privacy-preserving machine learning techniques Experience in building custom security tooling to enhance and automate security processes Interest in leveraging AI to automate security tasks and improve efficiency Contributions to open-source security projects or tools Experience in securing AI/ML models and data pipelines

Compensation and Benefits: $200,000 - $340,000 USD Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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You will work at the intersection of relationship management, commercial strategy, and product feedback. That means collaborating daily with technical, sales, legal, product, and operations teams to make sure clients don't just stay, they expand.

The ambition here is to grow revenue, deepen trust, and raise the bar for how Yuno shows up for its most important customers. We expect the same drive from you: always pushing for more, never settling for good enough when great is within reach.

This role also demands the kind of transparency that builds real partnerships. You will be the primary voice clients hear from Yuno , which means setting honest expectations, listening actively, and following through consistently.

At the same time, you will need to adapt fast. The payments landscape shifts constantly, and your ability to read the room, pivot your approach, and keep clients confident through change will be what sets you apart.

If you thrive on ownership, move fast, and know how to build trust at every level of a client organization , this role is for you.

Your Contribution Will Be:

• Serve as the primary point of contact for key accounts, building deep and lasting client relationships
• Drive revenue growth through upselling, cross-selling, and identifying new opportunities within existing accounts
• Monitor client health metrics and address risks or concerns proactively before they escalate
• Coordinate cross-functionally with Sales, Product, Operations, Legal, and Support to ensure seamless service delivery
• Translate client feedback into actionable insights for internal teams to improve products and processes
• Oversee account onboarding and ongoing performance, ensuring clients achieve their strategic objectives
• Prepare and deliver clear, compelling business reviews and strategic recommendations for client stakeholders
• Stay current on payment industry trends, regulations, and competitive dynamics to bring value beyond the contract

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Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use.

Making data driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide tooling and guidance to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively.

Responsibilities

• Contribute towards the long-term technical roadmap for data-driven and machine learning iteration at Plaid
• Leading key data infrastructure projects such as improving ML development golden paths, implementing offline streaming solutions for data freshness, building net new ETL pipeline infrastructure, and evolving data warehouse or data lakehouse capabilities.
• Working with stakeholders in other teams and functions to define technical roadmaps for key backend systems and abstractions across Plaid.
• Debugging, troubleshooting, and reducing operational burden for our Data Platform.
• Growing the team via mentorship and leadership, reviewing technical documents and code changes.

Qualifications

• 5+ years of software engineering experience
• Extensive hands-on software engineering experience, with a strong track record of delivering successful projects within the Data Infrastructure or Platform domain at similar or larger companies.
• Deep understanding of one of: ML Infrastructure systems, including Feature Stores, Training Infrastructure, Serving Infrastructure, and Model Monitoring OR Data Infrastructure systems, including Data Warehouses, Data Lakehouses, Apache Spark, Streaming Infrastructure, Workflow Orchestration.
• Strong cross-functional collaboration, communication, and project management skills, with proven ability to coordinate effectively.
• Proficiency in coding, testing, and system design, ensuring reliable and scalable solutions.
• Demonstrated leadership abilities, including experience mentoring and guiding junior engineers.

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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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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That’s what our Risk product engineering teams do at Mercury. We guide thousands of business applications through our systems,each one unique, each one needing a smooth and trustworthy landing. The challenge: keep everything moving fast without compromising safety. Every day, our work helps founders open their first account, launch their next idea, and accelerate their growth like rocketships.

Our mission is to build the intelligent, automated systems and operational tools that make this possible,where machine learning, AI, and human judgment work seamlessly together to power the next generation of business banking*. We use intelligence to detect risks earlier, make real-time decisions with confidence, and enable instant, delightful account approvals that keep pace with the builders we serve.

When we do it right, the result is quiet brilliance: onboarding that feels effortless, even though it’s powered by galaxies of data, precision, and care.

We’re looking for a Staff Software Engineer to contribute with building the systems and tools that make it all happen,from application approvals to ongoing and enhanced due diligence,ensuring every business that joins Mercury is both safe and their experience is delightful.

As part of this role, you will:

• Lead the architecture, implementation, and long-term roadmap for core systems which support multiple fraud prevention use cases.
• Own the end-to-end delivery of large cross-function projects, translating ambiguous high impact problems into strategy and execution, make pragmatic tradeoffs, and drive teams to measurable outcomes.
• Design, build, and operate highly available, low-latency, backend systems that enable real-time scoring and decisioning for fraud prevention.
• Partner with Data Science and ML teams to productionize models, build reliable ML data pipelines, and enable real-time model serving.
• Establish and elevate department level best practices, review designs, drive engineering quality, and act as a trusted advisor on architectural tradeoffs.
• Mentor and grow engineers, documenting learnings and sharing technical direction through writing, 1:1s, and team meetings
• Navigate and influence multiple stakeholders, help align teams, communicate tradeoffs to technical and non-technical partners, and independently resolve cross team issues.

The ideal candidate for the role:

• Has 7-10+ years of software development experience, with a strong focus on the backend, with a knowledge of or excitement to learn Haskell.
• Has been an experienced technical lead making architectural decisions in the past and seen the impact of those decisions over time. You should be able to clearly articulate your technical opinions and lay out tradeoffs.
• Is passionately product-minded and has experience building and shipping new products balancing reliability and velocity.
• Is someone comfortable driving discussions in areas with ambiguous ownership, approaches them with empathy, and delights in getting outcomes. Our work touches many other teams and areas of the product; you’ll have a lot of autonomy and the expectation is you’ll use that to seek out ways to have an impact.
• Is ridiculously helpful, taking initiative to make your coworkers’ lives easier by investing time to mentor and proactively share your knowledge and learnings through writings, 1:1s, and team meetings.
• Experience in fintech, fraud systems, or the broader risk domain is a strong plus.

If this role interests you, we invite you to explore our public demo at personal-demo.mercury.com .

The total rewards package at Mercury includes base salary, equity (stock options), and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.

Our target new hire base salary ranges for this role are the following:

• US employees (any location): $239,000 - $298,800
• Canadian employees (any location): CAD 225,900 - 282,400

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Forge turns cutting-edge research into enterprise-ready capabilities by powering model fine-tuning, reinforcement learning and post-training workflows. By working at the intersection of research and product it provides customers with the tools to train specialized models that deliver real-world business value.

As the PM leading Forge you will shape a 0-1 product with significant business impact and the potential to grow offering while defining how organizations train and deploy the next generation of AI models.

Key Responsibilities:

Define the Future • Set the vision: Shape and evangelize a compelling product strategy for Forge ensuring alignment with company goals and market opportunities.

Spot the gaps: Lead market and UX research to uncover unmet needs, competitive whitespaces, and emerging trends in SOTA AI post-training capabilities.

Build & Ship • Own the lifecycle: Drive end-to-end product development, from ideation to launch and iteration,balancing speed, quality, and user delight.

Champion the user: Partner with design and research to craft intuitive, high-impact experiences, using data and feedback to refine continuously.

Scale, Execute, & Enable • Go-to-market: Collaborate with marketing and sales to launch products successfully, including pricing, positioning, and adoption strategies.

Align stakeholders: Rally engineering, design, and business teams around priorities, trade-offs, and timelines.

Prioritize ruthlessly: Maintain a dynamic roadmap that delivers quick wins while advancing long-term bets.

Requirements:

Product Management Experience 5+ years of relevant experience in new, competitive, fast-paced and ambiguous environments with a track record of building and scaling complex AI/ML or infrastructure solutions.

Technical skills - Very good understanding of training pipelines, RL loops, and model deployment architectures,

Expertise in AI model lifecycle management, including fine-tuning, evaluation, and serving.

Experience with Infrastructure as Code (IaC), containerization, and scalable deployment modes (e.g., on-prem, VPC, cloud).

Familiarity with Kubernetes/Slurm is a strong plus.

User obsession Relentless focus on solving real user problems, backed by data and qualitative insights.

Cross-functional influence Proven ability to align and inspire engineering, design, and go-to-market teams without direct authority.

Problem-solving Balance big-picture thinking with hands-on problem-solving , you’re equally comfortable crafting a roadmap, diving into metrics and running technical tests.

Communication: Crisp, persuasive storytelling for executives, teams, and users , ability to distill complex technical concepts (e.g., RL, LoRA, SFT) into clear narratives for docs, decks, and workshops.

Adaptability: Thrive in high-velocity, dynamic settings where priorities shift quickly.

Collaboration: Low ego + high EQ , you build trust and drive decisions through clarity, not hierarchy.

Autonomy: Self-directed with a bias for action, you own outcomes end-to-end.

Preferred Qualifications:

Infrastructure knowledge - Strong knowledge of model training, model architectures, etc.

Strong understanding how complex architectures are designed and impact of deployment modes

Proficient coding skills are strongly recommended

Kubernetes know-how strongly recommended

Growth mindset: Deep familiarity with product-led growth strategies (e.g., viral loops, onboarding optimization, monetization, etc.).

Builder’s mindset: Founder or early-stage PM experience , you’ve turned 0 → 1 ideas into products users love.

Technical depth: Ability to prototype, hack, or dive into code when needed.

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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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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.

Build human-in-the-loop review workflows for AI decisions that require oversight , particularly for high-value financial actions.

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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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.

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.

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.

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.

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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You should have a deep understanding of generative AI, hands-on experience serving generative deep learning models in production settings, and a track record of working directly with customers, iterating on solutions, and providing tailored support. Proficiency in Python and intuitive understanding of API integrations are also essential. Excellent communication skills, honed through collaborating with non-technical stakeholders, are necessary to adapt your message depending on who's in the room.

Prior experience finetuning diffusion models, working with customization tools like ComfyUI, and contributing to open-source projects in the diffusion model space are highly valued. Deploying models on cloud platforms using state-of-the-art serving infrastructure is also desirable.

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As Head of Infrastructures Services, Financial Infrastructure, you have the opportunity to help shape and build the infrastructure-as-a-service business within Anchorage’s Financial Infrastructure group.

This#%% role is highly technical, focused on developing, managing, and executing the overall business strategy, informing and overseeing the product roadmap, and enabling cross-function coordination to launch, operate, and grow this service.

Within this role, you are considered a subject matter expert in financial infrastructure products, including banking-as-a-service and software-as-a-service models, inclusive of the associated legal, risk, and compliance considerations.

This is an internal- and external-facing role, requiring leadership of cross-functional teams to drive strategic initiatives, gain consensus among stakeholders, and manage against internal deadlines.

You are also responsible for interacting with current and prospective clients to establish feedback loops on products and services, establishing pricing and revenue models, and supporting the client support model.

Beyond strategy development and execution, you are expected to support the day-to-day business operations of the Financial Infrastructure line of businesses, which includes internal reporting, business development, go-to-market, and sales efforts.

Technical Skills:

• Applied extensive knowledge of current and emerging trends in fintech, banking-as-a-service, brokerage, and/or payments to provide strategic direction, guidance, and leadership within the Financial Infrastructure business.

• Working knowledge of the risk, compliance, legal, and regulatory considerations related to providing regulated financial infrastructure services.

• Demonstrates and promotes discipline against strategic objectives while serving as a strategic leader internally and externally.

• Very strong writing, presentation, and public communication skills.

Complexity and impact of work:

• Architect and launch a new, high-impact business unit within Anchorage, integrating Financial Infrastructure and Product Engineering teams to deliver scalable infrastructure-as-a-service.

• Direct the end-to-end life-cycle of the product roadmap, ensuring that technical execution aligns with the broader business strategy.

• Serve as the path owner for strategy execution, determining the methods and procedures necessary to move the business forward against internal deadlines.

• Collaborate across internal stakeholders, including Sales, Legal, Risk, and Compliance, to define strategy, prioritization, and resolve roadblocks to ensure the infrastructure-as-a-service model is both robust and compliant.

• Independently document, execute, and report on strategic proposals, determining methods and procedures along the way to drive business forward.

• Cultivate deep organizational knowledge and leverage senior-level, client-facing influence to ensure that infrastructure reconfiguration align with both internal capabilities and the strategic needs of our institutional clients.

• Operate in a high-ambiguity and fast-moving environment who can rapidly translate strategy into technical execution and operational results.

Organizational Knowledge:

• Deep knowledge of Anchorage’s core business operations, including operations, customer experience, legal, and compliance to ensure new infrastructure services are seamlessly integrated.

• Play an active role to promote, support, and grow the newly built business unit within the broader strategic priorities and product engineering goals of Anchorage.

• Direct the prioritization and development of key product features and automation from a strategy and business perspective.

• Ensures knowledge sharing across the FinFra team.

Communication and influence:

• Documents strategic proposals, business plans, and execution roadmaps to define the trajectory of the new Infrastructure business.

• Independently leads client-facing discussions and oversees business line deals, leveraging influence to establish feedback loops on services and products.

• Participates in speaking engagements and events to represent Anchorage as an SME in banking-as-a-service and software-as-a-service models.

• Crosses direct team and function boundaries to gain consensus, identify and solution issues, and present to senior stakeholders across Anchorage.

• Mentors and guides colleagues and team members on internal best practices, team strategies, and to promote discipline and understanding against the strategic goals of Anchorage and the Financial Infrastructure team.

• Serves as critical path owner and gatekeeper for strategy execution.

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

• Applied knowledge of trends in fintech, banking-as-a-service, brokerage, and/or payments to provide strategic direction, guidance, and leadership within the Financial Infrastructure business.

Additional Information Although not a requirement, bonus points if:

-You were emotionally moved by the soundtrack to Hamilton, which chronicles the founding of a new financial system. :)

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You will focus on process management and execution, ensuring our technical infrastructure initiatives run smoothly across global time zones while providing a structured framework for our engineering teams to succeed.

This is a high-impact Program Manager role with a strong focus on operations and program support within a mission-critical technical environment. You will be responsible for the program operations of our GenAI serving stack, bridging the gap between technical design and execution.

The role is dynamic and requires a leader who can thrive in an agile, fast-moving environment where priorities can shift quickly. You will manage critical workflows such as accelerating technical design reviews, enhancing cross-functional collaborations, and ensuring that the program's objectives are met with precision and efficiency.

Key responsibilities include defining and owning program charters, leading design reviews and technical steering discussion forums, partnering with engineers to resolve operational bottlenecks, and organizing program reviews and summits.

We are looking for a disciplined program leader who excels at bringing structure to complex environments and possesses the soft skills to influence stakeholders across different regions.

Requirements include proven experience as a Program Manager, experience working directly with engineers on large-scale infrastructure projects, and a demonstrated ability to create and implement process improvements.

Preferred qualifications include familiarity with software stacks, agile methodologies, and the AI/ML realm, specifically Gemini or similar LLM serving stacks, as well as experience working effectively with teams across multiple time zones.

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This role sits within Aladdin Platform Engineering and focuses on the infrastructure and platform services required to support machine learning models, large language models (LLMs), and emerging AI capabilities in production. The successful candidate will work closely with AI Engineers, Data Scientists, Platform Engineers, Security, and Product partners to deliver resilient, cloud native AI platforms in a highly regulated environment.

Key Responsibilities

• Design, build, and operate AI-focused infrastructure platforms supporting model development, training, evaluation, and inference.
• Engineer scalable, reliable, and secure cloud-native services to support AI workloads across AWS, Azure, and hybrid environments.
• Partner with AI Engineering and Data Science teams to improve developer experience, performance, and operational stability of AI systems.
• Enable production deployment of ML models and LLMs within governed enterprise environments, aligned with firmwide risk and compliance standards.
• Implement and maintain infrastructure as code and automation to ensure repeatable, auditable platform provisioning.
• Build and operate observability, monitoring, and alerting solutions for AI platforms, ensuring availability, performance, and cost transparency.
• Collaborate with Security and Risk partners to integrate identity, access controls, data protection, and governance into AI infrastructure.
• Contribute to architectural decisions and technical standards for AI platforms across Aladdin.
• Participate in on-call rotations and operational support as required for critical platforms.
• Continuously evaluate emerging AI infrastructure technologies and apply them pragmatically within BlackRock’s enterprise context.

Qualifications

• Strong experience in cloud infrastructure, platform engineering, or systems engineering roles.
• 4+ hands-on expertise with AWS and/or Azure and/or GCP, including Azure ML, Azure Foundry, AWS Bedrock, Google Vertex, as well as cloud compute, networking, storage, and security services.
• Understanding of ML platform operations and governance concepts, including model deployment strategies, lifecycle management, monitoring/observability, and Disaster Recovery
• Experience supporting LLMs, generative AI platforms, or model serving infrastructure.
• Experience supporting AI and machine learning workloads, with exposure to managed compute for model training and fine-tuning, experimentation over large datasets, and end-to-end MLOps pipeline flow including data ingestion, training, validation, and deployment.
• Proficiency with Infrastructure as Code tools (e.g., Terraform, ARM/Bicep, CloudFormation).
• Strong programming or scripting skills (e.g., Python, Bash, or similar).
• Experience building and operating containerized and Kubernetes-based platforms.
• Solid understanding of reliability, scalability, observability, and operational best practices.
• Ability to work effectively in cross-functional teams and communicate complex technical concepts clearly.

Our Benefits

To help you stay energized, engaged, and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge, and be there for the people you care about.

Our Hybrid Work Model

BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

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Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

Claude has your back. AIRE has Claude's. Help us keep Claude reliable for everyone who depends on it.

AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.

Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.

Responsibilities

• Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.
• Design and implement monitoring and observability systems across the token path.
• Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers
• Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.
• Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.

You may be a good fit if you

• Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs.
• Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.
• Think holistically about how systems compose and where the seams are.
• Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.
• Care about users and feel ownership over outcomes, even for systems you don't own.
• Have excellent communication and collaboration skills -- you'll be partnering across the entire company.
• Bring diverse experience -- the team's strength comes from people who've built product stacks, scaled databases, run massive distributed systems, and everything in between.

Strong candidates may also

• Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems
• Have experience operating large-scale model serving or training infrastructure (>1000 GPUs).
• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).
• Understand ML-specific networking optimizations like RDMA and InfiniBand.
• Have expertise in AI-specific observability tools and frameworks.
• Have experience with chaos engineering and systematic resilience testing.
• Have contributed to open-source infrastructure or ML tooling.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship

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

We encourage you to apply even if you do not believe you meet every single qualification.

Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us.

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and engineering as it does with computer science.

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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

AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.

Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.

Claude has your back. AIRE has Claude's. Help us keep Claude reliable for everyone who depends on it.

Responsibilities:

• Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.

• Design and implement monitoring and observability systems across the token path.

• Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers

• Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.

• Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.

You may be a good fit if you:

• Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs.

• Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.

• Think holistically about how systems compose and where the seams are.

• Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.

• Care about users and feel ownership over outcomes, even for systems you don't own.

• Have excellent communication and collaboration skills -- you'll be partnering across the entire company.

• Bring diverse experience -- the team's strength comes from people who've built product stacks, scaled databases, run massive distributed systems, and everything in between.

Strong candidates may also:

• Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems

• Have experience operating large-scale model serving or training infrastructure (>1000 GPUs).

• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).

• Understand ML-specific networking optimizations like RDMA and InfiniBand.

• Have expertise in AI-specific observability tools and frameworks.

• Have experience with chaos engineering and systematic resilience testing.

• Have contributed to open-source infrastructure or ML tooling.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

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

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as a team sport, where everyone contributes to the overall success of the team.

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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

Claude has your back. AIRE has Claude's. Help us keep Claude reliable for everyone who depends on it.

AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.

Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.

Responsibilities

• Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.
• Design and implement monitoring and observability systems across the token path.
• Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers
• Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.
• Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.

You may be a good fit if you

• Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs.
• Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.
• Think holistically about how systems compose and where the seams are.
• Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.
• Care about users and feel ownership over outcomes, even for systems you don't own.
• Have excellent communication and collaboration skills -- you'll be partnering across the entire company.
• Bring diverse experience -- the team's strength comes from people who've built product stacks, scaled databases, run massive distributed systems, and everything in between.

Strong candidates may also

• Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems
• Have experience operating large-scale model serving or training infrastructure (>1000 GPUs).
• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).
• Understand ML-specific networking optimizations like RDMA and InfiniBand.
• Have expertise in AI-specific observability tools and frameworks.
• Have experience with chaos engineering and systematic resilience testing.
• Have contributed to open-source infrastructure or ML tooling.

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Salary

The annual compensation range for this role is €235.000 - €295.000EUR.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and engineering as it does with computer science. We strive to build a team that reflects this perspective, with people from a wide range of backgrounds and disciplines.

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We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization, where you'll build and scale the critical infrastructure that powers our AI safety systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale.

Responsibilities:

• Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
• Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications
• Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
• Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
• Implement automated testing, deployment, and rollback systems for ML models in production safety applications
• Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

You may be a good fit if you:

• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)
• Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
• Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Care deeply about AI safety and the societal impacts of your work

Strong candidates may have experience with:

• Working with large language models and modern transformer architectures
• Implementing A/B testing frameworks and experimentation infrastructure for ML systems
• Developing monitoring and alerting systems for ML model performance and data drift
• Building automated labeling systems and human-in-the-loop workflows
• Experience in trust & safety, fraud prevention, or content moderation domains
• Knowledge of privacy-preserving ML techniques and compliance requirements
• Contributing to open-source ML infrastructure projects

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 the state of the art in AI safety and making a meaningful difference in the world.

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Anthropic's Safeguards team is responsible for the systems that allow us to deploy powerful AI models responsibly — and the data infrastructure underneath those systems is foundational to getting that right. The Safeguards Data Infrastructure team owns the offline data stack that underpins our safeguards work: the storage layer for sensitive user data, the tooling built on top of it, and the interfaces that let the rest of the Safeguards organisation access that data safely and ergonomically.

As Engineering Manager of this team, you'll be responsible for ensuring full portability of our safeguards data stack across an expanding set of deployment environments, building privacy-preserving data interfaces that enable ML and training workflows, and driving compliance with data regulations including HIPAA. This is a role at the intersection of infrastructure engineering, data privacy, and enterprise product requirements — and it sits at a critical juncture as Anthropic scales into new cloud environments and geographies

Responsibilities:

• Lead and grow a team of engineers delivering the data infrastructure and tooling that powers Anthropic's safeguards capabilities
• Own the strategy and execution for porting the safeguards offline data stack — including PII storage and tooling — across new cloud and deployment environments as Anthropic expands
• Build and maintain privacy-safe data APIs and interfaces that enable ML and training workflows while respecting data retention and access constraints
• Drive tooling and architecture decisions that maximise data retention within the bounds of our privacy and compliance requirements
• Manage privacy incident response processes and partner with compliance teams on regulatory requirements (e.g. HIPAA, EU privacy regulations)
• Collaborate closely with enterprise customers and product teams on zero data retention offerings, working balancing safety needs with robust enterprise data contracts
• Independently own and drive multiple workstreams, including planning, execution, and cross-team coordination
• Coach, mentor, and support the career development of your direct reports, helping them set and achieve their professional goals
• Partner with recruiting to attract, hire, and retain strong engineering talent

You may be a good fit if you:

• Have 4+ years of front-line engineering management experience
• Have a track record of leading teams that build and operate data infrastructure at scale
• Have hands-on software engineering experience as an individual contributor prior to moving into management
• Have a strong understanding of data privacy principles, PII handling, and compliance frameworks
• Are comfortable driving technical decisions in an ambiguous, fast-moving environment with competing priorities
• Have experience working cross-functionally across infrastructure, product, and compliance or security teams
• Are clear and persuasive communicators, both in writing and in person

Strong candidates may also:

• Have experience with multi-cloud or multi-region data portability, particularly in regulated environments
• Have built privacy-preserving data pipelines or interfaces for ML workloads
• Have experience with enterprise data contracts or zero data retention architectures
• Have explored novel approaches to data processing under strict access constraints, such as in-memory storage and compute for sensitive data
• Have a passion for building diverse and inclusive teams

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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

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

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Replit is experiencing extraordinary growth across our customer base and seeking a Mid-Market Account Manager with strong communication skills to drive retention, expansion, and customer success. Candidates with Customer Success or Account Management experience in SaaS, particularly with some technical background (i.e. having some coding knowledge or prior experience at other developer tool companies) are ideal.

We believe this role offers a distinctive opportunity for people who excel in client-facing situations and have a passion for customer success and AI. You'll leverage your skills to help customers maximize value from Replit's platform while driving sustainable revenue growth.

In this role you will:

• Serve as the primary relationship owner for your assigned customer portfolio, ensuring high satisfaction and retention
• Champion customer success by helping businesses realise the transformative potential of AI-powered software creation
• Lead onboarding programs that accelerate customer time-to-value and platform adoption
• Guide customers through Replit's rapidly evolving feature set, including Agent 3's autonomous capabilities, Plan Mode, and automation tools
• Execute renewal processes with a focus on expansion and long-term partnership development
• Maintain accurate customer health scores and renewal forecasting data in Replit's CRM (Hubspot)
• Gather and communicate valuable customer feedback, championing their interests within Replit
• Optimize customers' use of the Replit platform through ongoing training, support, and strategic guidance

Required skills and experience:

• 3-5+ years of experience in Customer Success, Commercial Account Management, or technical sales role, preferably in SaaS or developer tools
• Excellent communication skills, with the ability to explain technical concepts to non-technical audiences
• Proven track record of meeting or exceeding renewal and expansion revenue targets
• Experience managing customer relationships from onboarding through renewal and expansion
• Proficiency in using CRM systems and customer success tools (e.g., Hubspot)
• Ability to quickly learn and articulate the value of new technologies to diverse customer segments
• Strong problem-solving skills and consultative approach to customer challenges
• Self-motivated with excellent time management and organisational skills for managing multiple accounts
• Passion for technology and staying current with industry trends, particularly AI and automation
• Experience with or strong interest in AI is a plus
• Comfort with data analysis to drive customer success metrics and identify growth opportunities

Nice to have:

• You're an active Replit user
• You've worked at an early-stage startup or in developer tools
• Experience with rapid prototyping, product development workflows, or business process automation
• Background in customer success at companies serving technical or semi-technical user bases
• Degree in Computer Science, Engineering, Business, or a related field (or equivalent practical experience)

Tools + Tech Stack for this role:

• Replit
• Hubspot CRM
• Customer success and analytics platforms
• ZoomInfo
• LinkedIn Sales Navigator
• Hashboard, Hex
• Various customer communication and project management tools

This role may not be a fit if:

• You're not passionate about helping customers succeed and driving long-term relationships
• You lack an understanding of the software development lifecycle or have little interest in learning about technical workflows
• You're not excited about AI and its potential to transform how businesses operate
• You don't enjoy being in client-facing roles where 80% of your day is talking to customers
• You prefer transactional interactions over building deep, strategic relationships

_This is a full-time role that can be held from our NYC office. This role has an in-office requirement._

Full-Time Employee Benefits Include:

💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🚗 Commuter Benefits 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement 🏝 Flexible Time Off (FTO) + Holidays 🚀 Quarterly Team Gatherings ☕ In Office Amenities

Want to learn more about what we are up to?

• Meet the Replit Agent
• Replit: Make an app for that
• Replit Blog
• Amjad TED Talk

Interviewing + Culture at Replit

• Operating Principles
• Reasons not to work at Replit

To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome

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In this role, you will own cloud security across GCP (primary) and supplemental environments in AWS and Azure, as well as containerized systems, SaaS platforms, and our multi-tenant AI infrastructure. You’ll improve our security posture through strong architecture, posture management, secure-by-default development practices, and close partnership with Engineering, Compliance, Security Architecture, and Platform teams.

This is a highly impactful, hands-on leadership role—perfect for someone who wants to solve complex security challenges at scale while influencing product, engineering, and go-to-market teams.

Cloud Security Engineering

• Lead configuration hardening across GCP, with additional oversight of workloads and integrations running in AWS and Azure.
• Own and optimise CSPM platforms across multi-cloud environments—establishing configuration baselines, guardrails, and remediation workflows.
• Secure critical SaaS platforms, ensuring proper configurations, access controls, and engineering integrations.
• Lead infrastructure vulnerability management across multi-cloud systems, containers, registries, and platform services.
• Enhance security across containerised and Kubernetes (GKE/EKS/AKS) workloads, including runtime protections, network policies, and workload identity.
• Assess secure logging configurations across cloud/SaaS providers, ensuring audit logs, retention, and routing meet monitoring and architecture needs.

Secure Development & Architecture Enablement

• Partner with engineering teams to make services secure by default, embedding security into development workflows, CI/CD pipelines, and cloud-native deployments.

Cross-Functional Responsibilities

• Collaborate with Security Monitoring, Compliance/GRC, Architecture, DevOps, Platform Engineering, and ML Infrastructure.
• Participate in communicating security advisories, best practices, and updates to Replit’s customers.
• Support incident investigations as a cloud security subject-matter expert.

Required Skills & Experience:

• 7+ years of experience in cloud engineering, with 3+ years in a senior or lead role.
• Hands-on experience with CSPM tools (Wiz, Lacework, Prisma, Orca, SCC, etc.).
• Deep expertise in GCP security (IAM, VPC, KMS, GKE, Cloud Logging).
• Experience securing and governing SaaS platforms and identity integrations.
• Operational experience with infrastructure vulnerability management across cloud and container environments.
• Working knowledge of AWS and/or Azure security services and configurations.
• Experience with container and Kubernetes security across GKE, EKS, or AKS.
• Strong IaC security experience with Terraform, Pulumi, or similar tooling.
• Familiarity with compliance standards (SOC 2, ISO 27001, PCI DSS).

Preferred Qualifications:

• Experience supporting engineering teams in building secure-first, cloud-native or PaaS environments.
• Background securing AI/ML pipelines, model-serving infrastructure, or developer platform services.
• Experience in high-growth technology or cloud-native product companies.
• Experience with securing AI/agentic systems and sensitive data pipelines.
• Automation/scripting with Python.
• Relevant certifications (e.g., GCP Professional Cloud Security Engineer, AWS/Azure security certs).

What We Value:

• Problem-solving mindset — Ability to break down complex security and operational challenges into clear engineering solutions.
• Autonomy — Comfortable leading initiatives, collaborating effectively, and driving outcomes with minimal oversight.
• Communication excellence — Able to translate deep technical concepts for engineers, executives, and enterprise customers.
• Continuous learning — Passion for staying current with AI security, cloud-native advances, and emerging threats.
• Automation-first approach — Belief in reducing operational toil and building scalable, self-healing systems.

Full-Time Employee Benefits Include:

• Competitive Salary & Equity
• 401(k) Program with a 4% match
• Health, Dental, Vision and Life Insurance
• Short Term and Long Term Disability
• Paid Parental, Medical, Caregiver Leave
• Commuter Benefits
• Monthly Wellness Stipend
• Autonomous Work Environment
• In Office Set-Up Reimbursement
• Flexible Time Off (FTO) + Holidays
• Quarterly Team Gatherings
• In Office Amenities

XML job scraping automation by YubHub

Replit is experiencing extraordinary growth across our customer base and seeking an Enterprise Account Manager with strong communication skills to drive retention, expansion, and customer success. Candidates with Customer Success or Enterprise Account Management experience in SaaS, particularly with some technical background (i.e. having some coding knowledge or prior experience at other developer tool companies) are ideal.

We believe this role offers a distinctive opportunity for people who excel in client-facing situations and have a passion for customer success and AI. You'll leverage your skills to help customers maximize value from Replit's platform while driving sustainable revenue growth.

In this role you will:

• Serve as the primary relationship owner for your assigned customer portfolio, ensuring high satisfaction and retention
• Champion customer success by helping businesses realise the transformative potential of AI-powered software creation
• Lead onboarding programs that accelerate customer time-to-value and platform adoption
• Guide customers through Replit's rapidly evolving feature set, including Agent 3's autonomous capabilities, Plan Mode, and automation tools
• Execute renewal processes with a focus on expansion and long-term partnership development
• Maintain accurate customer health scores and renewal forecasting data in Replit's CRM (Hubspot)
• Gather and communicate valuable customer feedback, championing their interests within Replit
• Optimize customers' use of the Replit platform through ongoing training, support, and strategic guidance

Required skills and experience:

• 3-5+ years of experience in Customer Success, Enterprise Account Management, or technical sales role, preferably in SaaS or developer tools
• Excellent communication skills, with the ability to explain technical concepts to non-technical audiences
• Proven track record of meeting or exceeding renewal and expansion revenue targets
• Experience managing customer relationships from onboarding through renewal and expansion
• Proficiency in using CRM systems and customer success tools (e.g., Hubspot)
• Ability to quickly learn and articulate the value of new technologies to diverse customer segments
• Strong problem-solving skills and consultative approach to customer challenges
• Self-motivated with excellent time management and organisational skills for managing multiple accounts
• Passion for technology and staying current with industry trends, particularly AI and automation
• Experience with or strong interest in AI is a plus
• Comfort with data analysis to drive customer success metrics and identify growth opportunities

Nice to have:

• You're an active Replit user
• You've worked at an early-stage startup or in developer tools
• Experience with rapid prototyping, product development workflows, or business process automation
• Background in customer success at companies serving technical or semi-technical user bases
• Degree in Computer Science, Engineering, Business, or a related field (or equivalent practical experience)

Tools + Tech Stack for this role:

• Replit
• Hubspot CRM
• Customer success and analytics platforms
• ZoomInfo
• LinkedIn Sales Navigator
• Hashboard, Hex
• Various customer communication and project management tools

This role may _not_ be a fit if:

• You're not passionate about helping customers succeed and driving long-term relationships
• You lack an understanding of the software development lifecycle or have little interest in learning about technical workflows
• You're not excited about AI and its potential to transform how businesses operate
• You don't enjoy being in client-facing roles where 80% of your day is talking to customers
• You prefer transactional interactions over building deep, strategic relationships

_This is a full-time role that can be held from our Foster City, CA office. This role has an in-office requirement of Monday, Wednesday, and Friday._

Full-Time Employee Benefits Include:

💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🚗 Commuter Benefits 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement 🏝 Flexible Time Off (FTO) + Holidays 🚀 Quarterly Team Gatherings ☕ In Office Amenities

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Replit provides the fastest way to turn ideas into software. With our powerful AI-powered Agent and Assistant, anyone can create and launch apps from natural language in just one click. Build and deploy full-stack applications directly from your browser—no setup required. Never written a line of code in your life? No problem. Replit makes software creation accessible, collaborative, and lightning-fast.

Join us in our mission to empower the next generation of builders. As an Enterprise Account Manager, you will leverage your skills to help customers maximize value from Replit's platform while driving sustainable revenue growth.

Responsibilities:

• Serve as the primary relationship owner for your assigned customer portfolio, ensuring high satisfaction and retention
• Champion customer success by helping businesses realise the transformative potential of AI-powered software creation
• Lead onboarding programs that accelerate customer time-to-value and platform adoption
• Guide customers through Replit's rapidly evolving feature set, including Agent 3's autonomous capabilities, Plan Mode, and automation tools
• Execute renewal processes with a focus on expansion and long-term partnership development
• Maintain accurate customer health scores and renewal forecasting data in Replit's CRM (Hubspot)
• Gather and communicate valuable customer feedback, championing their interests within Replit
• Optimize customers' use of the Replit platform through ongoing training, support, and strategic guidance

Requirements:

• 3-5+ years of experience in Customer Success, Enterprise Account Management, or technical sales role, preferably in SaaS or developer tools
• Excellent communication skills, with the ability to explain technical concepts to non-technical audiences
• Proven track record of meeting or exceeding renewal and expansion revenue targets
• Experience managing customer relationships from onboarding through renewal and expansion
• Proficiency in using CRM systems and customer success tools (e.g., Hubspot)
• Ability to quickly learn and articulate the value of new technologies to diverse customer segments
• Strong problem-solving skills and consultative approach to customer challenges
• Self-motivated with excellent time management and organisational skills for managing multiple accounts
• Passion for technology and staying current with industry trends, particularly AI and automation
• Experience with or strong interest in AI is a plus
• Comfort with data analysis to drive customer success metrics and identify growth opportunities

Nice to have:

• You're an active Replit user
• You've worked at an early-stage startup or in developer tools
• Experience with rapid prototyping, product development workflows, or business process automation
• Background in customer success at companies serving technical or semi-technical user bases
• Degree in Computer Science, Engineering, Business, or a related field (or equivalent practical experience)

Tools + Tech Stack for this role:

• Replit
• Hubspot CRM
• Customer success and analytics platforms
• ZoomInfo
• LinkedIn Sales Navigator
• Hashboard, Hex
• Various customer communication and project management tools

This role may not be a fit if:

• You're not passionate about helping customers succeed and driving long-term relationships
• You lack an understanding of the software development lifecycle or have little interest in learning about technical workflows
• You're not excited about AI and its potential to transform how businesses operate
• You don't enjoy being in client-facing roles where 80% of your day is talking to customers
• You prefer transactional interactions over building deep, strategic relationships

Full-Time Employee Benefits Include:

• Competitive Salary & Equity
• 401(k) Program with a 4% match
• Health, Dental, Vision and Life Insurance
• Short Term and Long Term Disability
• Paid Parental, Medical, Caregiver Leave
• Commuter Benefits
• Monthly Wellness Stipend
• Autonomous Work Environment
• In Office Set-Up Reimbursement
• Flexible Time Off (FTO) + Holidays
• Quarterly Team Gatherings
• In Office Amenities

Want to learn more about what we are up to?

• Meet the Replit Agent
• Replit: Make an app for that
• Replit Blog
• Amjad TED Talk

Interviewing + Culture at Replit

• Operating Principles
• Reasons not to work at Replit

To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world

XML job scraping automation by YubHub

San Mateo, CA, United States

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.

At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.

A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

Natural Language Processing (NLP) is central to enabling massive-scale communication, creation, and safety across the Roblox platform. This role offers the unique opportunity to build and deploy cutting-edge NLP, speech, and generative AI models that operate at an unprecedented scale, impacting hundreds of millions of daily users.

You will solve an extremely diverse range of high-scale language-related problems—from real-time moderation of voice and text to automatically localizing experiences and empowering users through LLM-driven creation tools. We combine cutting-edge research with large-scale engineering to bridge experimentation and production, designing algorithms that shape the next generation of language services for our immersive, user-generated content platform.

Teams Hiring for This Role

• Safety AI Systems:Dedicated to building end-to-end ML systems for maintaining civility and safety across the platform, operating at massive scale. This includes:

• Real-time Moderation: Building world-class NLP and speech models for real-time moderation of voice and text (processing over 6 billion messages daily) and advanced interventions that measurably improve user civility.

• Critical Harms & Advanced Detection: Developing specialized LLM agents, behavioral analysis, and graph systems for detecting and preventing rare, high-risk scenarios (e.g., child safety, terrorism), requiring adversarial thinking and multi-step reasoning.

• Safety Data Quality: Ensuring all Safety AI systems are robust by managing the core data infrastructure, MLOps, and Active Learning initiatives for continuous model improvement.

You Will

• Design and implement deep learning-based NLP and speech solutions that address problems across Roblox, from creation to safety.

• Develop advanced models, including Large Language Models (LLMs), machine translation, and generative AI, for user interactions, content creation, and moderation.

• Have the independence and end-to-end responsibility to develop NLP/ML-based services that are scalable and resilient.

• Be a technical bar-raiser for cutting-edge ML technology, high code quality, and architectural designs.

• Work backward from user and product needs to deliver ML solutions that drive engagement, safety, and ecosystem growth.

You Have

• Possessing or pursuing a Ph.D. in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field, with a thesis aligned to Roblox’s research areas.

• Expertise in one or more areas: NLP, Speech Models, Large Language Models, Machine Translation, or Generative AI (including diffusion models).

• Experience with transformer-based model design, training, serving, and product integration.

• A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues (e.g., ACL, EMNLP, Interspeech, ICML, NeurIPS).

• Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.

You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.

As you apply, you can find more information about our process by signing up for Speak\_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.

For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.

Annual Salary Range

$195,780—$242,100 USD

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).

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Microsoft are looking for a highly experienced Principal Software Engineer to join their Ads Engineering Platform team to design and build next-generation Ads products that drive revenue growth and create innovative advertising experiences for users and advertisers.

About the Role

We are seeking a highly experienced software engineer to join our Ads Engineering Platform team to design and build next-generation Ads products that drive revenue growth and create innovative advertising experiences for users and advertisers. You will play a key role in evolving the core capabilities of our ad-serving infrastructure—the engine that powers advertising across Bing Search, MSN, Microsoft Start, and shopping experiences in Microsoft Edge. Our serving stack operates at massive global scale, delivering millions of ad requests per second through a geo-distributed, low-latency system that integrates real-time bidding, intelligent ranking, and ML-driven decisioning pipelines. We leverage a mix of CPU and GPU-based inference to balance latency, throughput, and cost efficiency. This role combines product innovation, distributed systems architecture, and performance engineering. You will help shape both new monetization capabilities and the next generation of model serving infrastructure that powers them.

Accountabilities

• Design and build new Ads products and monetization capabilities that unlock incremental revenue and enhance advertiser and end-user experiences.
• Lead the development of large-scale, distributed online serving systems to process millions of ad requests per second with ultra-low latency and high reliability.

The Candidate we're looking for

Experience:

• 10+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

Technical skills:

• Proficient experience designing and operating real-time online serving or ranking systems.
• Proficient understanding of distributed systems fundamentals: concurrency, multi-threading, memory management, networking, and fault tolerance.

Personal attributes:

• Demonstrated ability to diagnose performance bottlenecks and improve latency, throughput, and cost efficiency in high-traffic systems.

Benefits

• Competitive salary range of $163,000 - $296,400 per year.
• Benefits and other compensation.
• Opportunities for professional growth and development.

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Microsoft AI are looking for a talented Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. We're looking for someone who will bring an abundance of positive energy, empathy, and kindness to the team every day, in addition to being highly effective.

About the Role

We are seeking a highly skilled and experienced engineer to join our team as a Member of Technical Staff, Multimodal Infrastructure. The successful candidate will be responsible for designing, developing, and maintaining large-scale multimodal data processing pipelines, model pretraining and post-training frameworks, and model inference and serving frameworks. They will work closely with research scientists and product engineers to solve infra-related problems and find a path to get things done despite roadblocks to get their work into the hands of users quickly and iteratively.

Accountabilities

• Design, develop, and maintain large-scale multimodal data processing pipelines.
• Design, develop, and maintain large-scale multimodal model pretraining and post-training frameworks.
• Design, develop, and maintain large-scale multimodal model inference and serving frameworks.

The Candidate we're looking for

Experience:

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

Technical skills:

• Strong proficiency in distributed data processing infra (resource utilization management, fault tolerance, ray & spark) and CPU/GPU batch processing optimizations.
• Experience with state-of-art model inference and serving frameworks.
• Experience with image/video/audio data processing.
• Experience with common data formats for efficient I/O.

Personal attributes:

• Enjoy working in a fast-paced, design-driven, product development cycle.
• Embody our Culture and Values.

Benefits

• Competitive salary and benefits package.
• Opportunities for professional growth and development.
• Collaborative and dynamic work environment.
• Access to cutting-edge technology and tools.
• Flexible work arrangements.

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Microsoft AI are looking for a talented Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. We're looking for someone who will bring an abundance of positive energy, empathy, and kindness to the team every day, in addition to being highly effective.

About the Role

We're looking for someone who will design, develop and maintain large-scale multimodal data processing pipelines, model pretraining and post-training frameworks, and model inference and serving frameworks. You will work closely with research scientists and product engineers on multimodal data processing, model training, inference and serving tasks. As a contributing member of the core group of engineers, you would also bring to the table best practices driving architectural changes and influence roadmap of relevant software and hardware components.

Accountabilities

• Design, develop and maintain large-scale multimodal data processing pipelines.
• Design, develop and maintain large-scale multimodal model pretraining and post-training frameworks.
• Design, develop and maintain large-scale multimodal model inference and serving frameworks.

The Candidate we're looking for

Experience:

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

Technical skills:

• Strong proficiency in distributed data processing infra (resource utilization management, fault tolerance, ray & spark) and CPU/GPU batch processing optimizations.
• Experience with state-of-art model inference and serving frameworks.
• Experience with image/video/audio data processing.
• Experience with common data formats for efficient I/O.

Personal attributes:

• Enjoy working in a fast-paced, design-driven, product development cycle.
• Embody our Culture and Values.

Benefits

• Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.
• Comprehensive health and wellbeing benefits.
• Professional development opportunities.
• Financial benefits (bonus, equity, pension, etc.).

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Microsoft AI are looking for a talented Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. We’re looking for someone who will bring an abundance of positive energy, empathy, and kindness to the team every day, in addition to being highly effective.

About the Role

As a Member of Technical Staff, Multimodal Infrastructure, you will be responsible for designing, developing, and maintaining large-scale multimodal data processing pipelines, model pretraining and post-training frameworks, and model inference and serving frameworks. You will work closely with research scientists and product engineers to solve infra-related problems and find a path to get things done despite roadblocks to get your work into the hands of users quickly and iteratively.

Accountabilities

• Design, develop, and maintain large-scale multimodal data processing pipelines.
• Design, develop, and maintain large-scale multimodal model pretraining and post-training frameworks.
• Design, develop, and maintain large-scale multimodal model inference and serving frameworks.

The Candidate we're looking for

Experience:

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

Technical skills:

• Strong proficiency in distributed data processing infra (resource utilization management, fault tolerance, ray & spark) and CPU/GPU batch processing optimizations.
• Experience with state-of-art model inference and serving frameworks.
• Experience with image/video/audio data processing.
• Experience with common data formats for efficient I/O.

Personal attributes:

• Enjoy working in a fast-paced, design-driven, product development cycle.
• Embody our Culture and Values.

Benefits

• Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.
• Comprehensive health and wellbeing benefits.
• Professional development opportunities.
• Financial benefits (bonus, equity, pension, etc.).

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What you'll do

As Site Lead, you'll serve as the face of Perplexity in London, responsible for building our technical organization, fostering a world-class engineering culture, and directly managing one or more infrastructure teams. You'll report to senior leadership and work cross-functionally with teams across our global footprint.

What you need

• 10+ years of experience in software engineering with 5+ years in infrastructure, cloud infrastructure, or AI infrastructure roles
• 3+ years of people management experience, including building and scaling teams
• Proven track record of establishing or significantly growing an engineering site or office

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