ac45e205-e7d Engineering Manager, Inference Routing and Performance About the role\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what's already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\n\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn't have been shed.\n\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic's inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\n\nAs Anthropic moves from "many independent inference replicas" toward "a single warehouse-scale computer running a coordinated program," Dystro is the coordination layer. This is a deeply technical team.\n\nThe engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators.\n\nThey work shoulder-to-shoulder with teams that write kernels and ML framework internals.\n\nThe EM for this team doesn't need to write kernels , but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet.\n\nYou'll inherit a strong team of distributed-systems engineers, and you'll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence.\n\nThe job is holding both.\n\n## Representative work:\nThings the Inference Routing EM actually spends time on:\n- Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses\n- Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers\n- Working through a persistent tail-latency regression with the team , walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack\n- Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win\n- Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick\n- Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they'd be additive to a team that already goes deep\n\n## What you'll do:\nDrive system-level performance\n- Own the technical roadmap for cluster-level inference efficiency , routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync\n- Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results\n- Build the team's habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is\n\nDeliver reliably and operate cleanly\n- Set technical strategy for how routing evolves across heterogeneous hardware (GPUs, TPUs, Trainium) and across all our serving surfaces\n- Run the team's operational backbone , on-call rotation, incident response, postmortem review, deploy safety , so the team can ship aggressively without the system becoming fragile\n- Create clarity at a seam: Inference Routing sits between the API surface, the inference engines, and the cloud deployment teams. You'll make sure commitments are realistic, dependencies are understood, and nobody is surprised\n\nBuild and grow the team\n- Develop and retain a strong existing team, and hire against the bar described above: people who can go to the OS and framework level when the problem demands it, and who care about production reliability\n- Coach engineers through a roadmap where priorities shift with model launches, new hardware, and scaling demands. We pair a lot here , you'll help make that collaboration pattern productive\n- Pick up slack when it matters. This is a small team in a critical path; sometimes the EM is the one unblocking a stuck deploy or synthesizing a design debate\n\n## You may be a good fit if you:\n- Have 5+ years of engineering management experience, ideally with at least part of that leading teams on critical-path production infrastructure at scale\n- Have a deep systems background , load balancing, scheduling, cache-coherent distributed state, high-performance networking, or similar. You need enough depth to make architectural calls about routing and efficiency, and to evaluate candidates who go to the kernel and framework level\n- Have shipped performance improvements in large-scale systems and can explain, with numbers, what the impact was\n- Have run production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline\n- Are results-oriented with a bias toward impact, and comfortable working in a space where throughput, latency, stability, and feature velocity all pull in different directions\n- Build strong relationships across team boundaries , this is a seam role, and much of the job is making sure other teams can rely on yours\n- Are curious about machine learning systems. You don't need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems\n\nStrong candidates may also have:\n- Experience with LLM inference serving , KV caching, continuous batching, request scheduling, prefill/decode disaggregation\n- Background in cluster schedulers, load balancers, service meshes, or coordination planes at scale\n- Familiarity with heterogeneous accelerator fleets (GPU/TPU/Trainium) and how hardware differences affect workload placement\n- Experience with GPU/accelerator programming, ML framework internals, or OS-level performance debugging , enough to follow and evaluate the technical work, not necessarily to do it daily\n- Led teams at supercomputing or hyperscaler infrastructure scale\n- Led teams through rapid-growth periods where hiring and onboarding competed with roadmap delivery\n\nThe 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.\nAnnual Salary: $405,000-$485,000 USD

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]]> full-time senior hybrid $405,000-$485,000 USD engineering management, distributed systems, load balancing, scheduling, cache-coherent distributed state, high-performance networking, machine learning systems, LLM inference serving, cluster schedulers, load balancers, service meshes, coordination planes, heterogeneous accelerator fleets, GPU/TPU/Trainium, GPU/accelerator programming, ML framework internals, OS-level performance debugging Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic creates reliable, interpretable, and steerable AI systems. https://www.anthropic.com/ https://job-boards.greenhouse.io/anthropic/jobs/5155391008 San Francisco, CA | New York City, NY 2026-04-18 e9e3cff7-d9b Performance Engineer As a Performance Engineer at Anthropic, you will be responsible for identifying and solving novel systems problems that arise when running machine learning algorithms at scale. Your expertise will be crucial in developing systems that optimize the throughput and robustness of our largest distributed systems.

You will work closely with our team of researchers, engineers, and policy experts to build beneficial AI systems. Your contributions will have a direct impact on the development of our AI technology and its applications.

We are looking for a highly motivated and experienced engineer who is passionate about solving complex systems problems and has a strong background in software engineering or machine learning. If you are excited about the opportunity to work on cutting-edge AI technology and make a meaningful contribution to the field, we encourage you to apply.

Responsibilities:

  • Identify and solve novel systems problems that arise when running machine learning algorithms at scale
  • Develop systems that optimize the throughput and robustness of our largest distributed systems
  • Collaborate with our team of researchers, engineers, and policy experts to build beneficial AI systems
  • Contribute to the development of our AI technology and its applications

Requirements:

  • Significant software engineering or machine learning experience, particularly at supercomputing scale
  • Results-oriented, with a bias towards flexibility and impact
  • Ability to pick up slack, even if it goes outside your job description
  • Enjoy pair programming
  • Want to learn more about machine learning research
  • Care about the societal impacts of your work

Preferred qualifications:

  • Experience with high-performance, large-scale ML systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

Benefits:

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

Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process

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]]> full-time senior hybrid $280,000-$850,000 USD software engineering, machine learning, high-performance computing, GPU/Accelerator programming, ML framework internals, OS internals, language modeling with transformers, pair programming, results-oriented, flexibility and impact, ability to pick up slack, enjoy learning Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic is a public benefit corporation that creates reliable, interpretable, and steerable AI systems. https://www.anthropic.com https://job-boards.greenhouse.io/anthropic/jobs/4020350008 San Francisco, CA | New York City, NY | Seattle, WA 2026-04-18 63af8568-789 Engineering Manager, Inference Routing and Performance About the role\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what's already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\n\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn't have been shed.\n\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic's inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\n\nAs Anthropic moves from "many independent inference replicas" toward "a single warehouse-scale computer running a coordinated program," Dystro is the coordination layer. This is a deeply technical team.\n\nThe engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators.\n\nThey work shoulder-to-shoulder with teams that write kernels and ML framework internals.\n\nThe EM for this team doesn't need to write kernels , but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet.\n\nYou'll inherit a strong team of distributed-systems engineers, and you'll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence.\n\nThe job is holding both.\n\n## Representative work:\nThings the Inference Routing EM actually spends time on:\n- Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses\n- Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers\n- Working through a persistent tail-latency regression with the team , walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack\n- Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win\n- Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick\n- Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they'd be additive to a team that already goes deep\n\n## What you'll do:\nDrive system-level performance\n- Own the technical roadmap for cluster-level inference efficiency , routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync\n- Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results\n- Build the team's habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is\n\nDeliver reliably and operate cleanly\n- Set technical strategy for how routing evolves across heterogeneous hardware (GPUs, TPUs, Trainium) and across all our serving surfaces\n- Run the team's operational backbone , on-call rotation, incident response, postmortem review, deploy safety , so the team can ship aggressively without the system becoming fragile\n- Create clarity at a seam: Inference Routing sits between the API surface, the inference engines, and the cloud deployment teams. You'll make sure commitments are realistic, dependencies are understood, and nobody is surprised\n\nBuild and grow the team\n- Develop and retain a strong existing team, and hire against the bar described above: people who can go to the OS and framework level when the problem demands it, and who care about production reliability\n- Coach engineers through a roadmap where priorities shift with model launches, new hardware, and scaling demands. We pair a lot here , you'll help make that collaboration pattern productive\n- Pick up slack when it matters. This is a small team in a critical path; sometimes the EM is the one unblocking a stuck deploy or synthesizing a design debate\n\n## You may be a good fit if you:\n- Have 5+ years of engineering management experience, ideally with at least part of that leading teams on critical-path production infrastructure at scale\n- Have a deep systems background , load balancing, scheduling, cache-coherent distributed state, high-performance networking, or similar. You need enough depth to make architectural calls about routing and efficiency, and to evaluate candidates who go to the kernel and framework level\n- Have shipped performance improvements in large-scale systems and can explain, with numbers, what the impact was\n- Have run production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline\n- Are results-oriented with a bias toward impact, and comfortable working in a space where throughput, latency, stability, and feature velocity all pull in different directions\n- Build strong relationships across team boundaries , this is a seam role, and much of the job is making sure other teams can rely on yours\n- Are curious about machine learning systems. You don't need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems\n\nStrong candidates may also have:\n- Experience with LLM inference serving , KV caching, continuous batching, request scheduling, prefill/decode disaggregation\n- Background in cluster schedulers, load balancers, service meshes, or coordination planes at scale\n- Familiarity with heterogeneous accelerator fleets (GPU/TPU/Trainium) and how hardware differences affect workload placement\n- Experience with GPU/accelerator programming, ML framework internals, or OS-level performance debugging , enough to follow and evaluate the technical work, not necessarily to do it daily\n- Led teams at supercomputing or hyperscaler infrastructure scale\n- Led teams through rapid-growth periods where hiring and onboarding competed with roadmap delivery\n\nThe 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.\nAnnual Salary: $405,000-$485,000 USD

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]]> full-time senior hybrid $405,000-$485,000 USD engineering management, deep systems background, load balancing, scheduling, cache-coherent distributed state, high-performance networking, LLM inference serving, cluster schedulers, load balancers, service meshes, coordination planes, heterogeneous accelerator fleets, GPU/TPU/Trainium, GPU/accelerator programming, ML framework internals, OS-level performance debugging Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic creates reliable, interpretable, and steerable AI systems. https://www.anthropic.com/ https://job-boards.greenhouse.io/anthropic/jobs/5155391008 San Francisco, CA | New York City, NY 2026-04-18 1819a743-ca5 Engineering Manager, GPU (ML Accelerator) About the role:

As an Engineering Manager on Anthropic's performance and scaling teams, you will be responsible for ensuring your team identifies and removes bottlenecks, builds robust and durable solutions, and maximizes the efficiency of our systems.

Responsibilities:

  • Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
  • Become familiar with the team's technical stack enough to make targeted contributions as an individual contributor
  • Manage day-to-day execution of the team's work
  • Prioritize the team's work and manage projects in a highly dynamic, fast-paced environment
  • Coach and support your reports in understanding, and pursuing, their professional growth
  • Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

  • Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
  • Have a background in machine learning, AI, or a similar related technical field
  • Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
  • Excel at building strong relationships with stakeholders at all levels
  • Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
  • Have experience managing teams through periods of rapid growth and change

Strong candidates may also have experience with:

  • High-performance, large-scale ML systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

The annual compensation range for this role is $500,000-$850,000 USD.

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]]> full-time mid hybrid $500,000-$850,000 USD Machine Learning, AI, Performance or Distributed Systems, GPU/Accelerator Programming, ML Framework Internals, OS Internals, Language Modeling with Transformers Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic is a public benefit corporation that creates reliable, interpretable, and steerable AI systems. https://www.anthropic.com/ https://job-boards.greenhouse.io/anthropic/jobs/4741104008 San Francisco, CA | New York City, NY | Seattle, WA 2026-04-18 2bc207d0-89b Senior Machine Learning Engineer We are seeking a Senior Machine Learning Research Engineer to join the Machine Learning Science (MLS) team, within the Computational Science department. The ideal candidate has a strong knowledge in designing and building deep learning (DL) pipelines, and expertise in creating reliable, scalable artificial intelligence/machine learning (AI/ML) systems in a cloud environment.

The MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimising hardware utilisation for efficient training, and performing model optimisations.

As part of an interdisciplinary R&D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission.

Key responsibilities include:

  • Implementing and refining DL pipelines on distributed computing platforms to enhance the speed and efficiency of DL operations, including model training, data handling, model management, and inference.
  • Collaborating closely with ML scientists and software engineers to understand current challenges and requirements and ensure that the DL model development pipelines created are perfectly aligned with scientific goals and operational needs.
  • Continuously monitoring, evaluating, and optimising DL model training pipelines for performance and scalability.
  • Staying up to date with the latest advancements in AI, ML, and related technologies, and quickly learning and adapting new tools and frameworks, if necessary.
  • Developing and maintaining robust and reproducible DL pipelines that guarantee that DL pipelines can be reliably executed, maintaining consistency and accuracy of results.
  • Driving performance improvements across our stack through profiling, optimisation, and benchmarking. Implementing efficient caching solutions and debugging distributed systems to accelerate both training and evaluation pipelines.
  • Acting as a bridge facilitating communication between the engineering and scientific teams, documenting and sharing best practices to foster a culture of learning and continuous improvement.

Must-haves include:

  • MS or equivalent experience in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Software Engineering, with an emphasis on AI/ML theory and/or practical development.
  • 5+ years of post-MS industry experience working on developing AI/ML software engineering pipelines.
  • Proficiency in a general-purpose programming language: Python (preferred), Java, Julia, C, C++, etc.
  • Strong knowledge of ML and DL fundamentals and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Jax or Scikit-learn.
  • In-depth knowledge of scalable and distributed computing platforms that support complex model training (such as Ray or DeepSpeed) and their integration with ML developer tools like TensorBoard, Wandb, or MLflow.
  • Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how to deploy and manage AI/ML models and pipelines in a cloud environment.
  • Understanding of containerisation technologies (e.g., Docker) and computing resource orchestration tools (e.g., Kubernetes) for deploying scalable ML/AI solutions.
  • Proven track record of developing and optimising workflows for training DL models, large language models (LLMs), or similar for problems with high data complexity and volume.
  • Experience managing large datasets, including data storage (such as HDFS or Parquet on S3), retrieval, and efficient data processing techniques (via libraries and executors such as PyArrow and Spark).
  • Proficiency in version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices to maintain code quality and automate development workflows.
  • Expertise in building and launching large-scale ML frameworks in a scientific environment that supports the needs of a research team.
  • Excellent ability to work effectively with cross-functional teams and communicate across disciplines.

Nice-to-haves include:

  • Experience working with large-scale genomics or biological datasets.
  • Experience managing multimodal datasets, such as combinations of sequence, text, image, and other data.
  • Experience GPU/Accelerator programming and kernel development (such as CUDA, Triton or XLA).
  • Experience with infrastructure-as-code and configuration management.
  • Experience cultivating MLOps and ML infrastructure best practices, especially around reliability, provisioning and monitoring.
  • Strong track record of contributions to relevant DL projects, e.g. on github.

The US target range of our base salary for new hires is $161,925 - $227,325. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.

Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, colour, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

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]]> full-time senior hybrid $161,925 - $227,325 Python, Java, Julia, C, C++, PyTorch, TensorFlow, Jax, Scikit-learn, Ray, DeepSpeed, TensorBoard, Wandb, MLflow, AWS, Google Cloud, Azure, Docker, Kubernetes, Git, Continuous Integration/Continuous Deployment, Large-scale genomics or biological datasets, Multimodal datasets, GPU/Accelerator programming and kernel development, Infrastructure-as-code and configuration management, MLOps and ML infrastructure best practices Engineering Technology Freenome https://logos.yubhub.co/freenome.com.png Freenome is a quantitative biology company that aims to reduce cancer mortality via accessible early detection. https://freenome.com/ https://job-boards.greenhouse.io/freenome/jobs/8013673002 Brisbane, California 2026-04-17 b50d0ec9-1d8 Engineering Manager, ML Acceleration About Anthropic

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

About the role:

Anthropic's performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training. As an Engineering Manager on these teams you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems. You also will help bring clarity, focus, and context to your teams in a fast paced, dynamic environment.

Responsibilities:

  • Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
  • Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
  • Manage day-to-day execution of the team's work
  • Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment
  • Coach and support your reports in understanding, and pursuing, their professional growth
  • Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

  • Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
  • Have a background in machine learning, AI, or a similar related technical field
  • Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
  • Excel at building strong relationships with stakeholders at all levels
  • Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
  • Have experience managing teams through periods of rapid growth and change
  • Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you’ll need to understand (at a high level of abstraction) to be effective

Strong candidates may also have experience with:

  • High performance, large-scale ML systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

Logistics

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

Visa sponsorship:

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

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

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

Your safety matters to us.

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

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

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

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and paid time off, and a comprehensive benefits package.

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]]> full-time mid hybrid $500,000 - $850,000 USD Machine Learning, AI, Distributed Systems, High Performance Computing, GPU/Accelerator Programming, ML Framework Internals, OS Internals, Language Modeling with Transformers, High Performance, Large-Scale ML Systems, GPU/Accelerator Programming, ML Framework Internals, OS Internals, Language Modeling with Transformers Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic is a public benefit corporation that aims to create reliable, interpretable, and steerable AI systems. It has a quickly growing team of researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. https://job-boards.greenhouse.io https://job-boards.greenhouse.io/anthropic/jobs/4741104008 San Francisco, CA | New York City, NY | Seattle, WA 2026-03-08 797f344d-f9f Performance Engineer About the role:

Running machine learning (ML) algorithms at our scale often requires solving novel systems problems. As a Performance Engineer, you'll be responsible for identifying these problems, and then developing systems that optimize the throughput and robustness of our largest distributed systems. Strong candidates here will have a track record of solving large-scale systems problems and will be excited to grow to become an expert in ML also.

You may be a good fit if you:

  • Have significant software engineering or machine learning experience, particularly at supercomputing scale
  • Are results-oriented, with a bias towards flexibility and impact
  • Pick up slack, even if it goes outside your job description
  • Enjoy pair programming (we love to pair!)
  • Want to learn more about machine learning research
  • Care about the societal impacts of your work

Strong candidates may also have experience with:

  • High performance, large-scale ML systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

Representative projects:

  • Implement low-latency high-throughput sampling for large language models
  • Implement GPU kernels to adapt our models to low-precision inference
  • Write a custom load-balancing algorithm to optimize serving efficiency
  • Build quantitative models of system performance
  • Design and implement a fault-tolerant distributed system running with a complex network topology
  • Debug kernel-level network latency spikes in a containerized environment

Deadline to apply:

None. Applications will be reviewed on a rolling basis.

Logistics

  • Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
  • Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
  • Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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

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

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

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

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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Learn about our policy for using AI in our application process

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]]> full-time senior hybrid $280,000 - $850,000USD software engineering, machine learning, GPU/Accelerator programming, ML framework internals, OS internals, language modeling with transformers, high performance, large-scale ML systems, fault-tolerant distributed systems, complex network topology, quantitative models of system performance Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic is a public benefit corporation that creates reliable, interpretable, and steerable AI systems. The company is headquartered in San Francisco and has a team of researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. https://job-boards.greenhouse.io https://job-boards.greenhouse.io/anthropic/jobs/4020350008 San Francisco, CA | New York City, NY | Seattle, WA 2026-03-08 5897facf-31b Engineering Manager, Inference About the role:

Anthropic's performance and scaling teams focus on making the most efficient and impactful use of our compute resources, be it inference or training. As an Engineering Manager on these teams, you will be responsible for ensuring you and your team are identifying and removing bottlenecks, building robust and durable solutions, and maximizing the efficiency of our systems. You also will help bring clarity, focus, and context to your teams in a fast-paced, dynamic environment.

Responsibilities:

  • Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
  • Become familiar with the team's technical stack enough to make targeted contributions as an individual contributor
  • Manage day-to-day execution of the team's work
  • Prioritize the team's work and manage projects in a highly dynamic, fast-paced environment
  • Coach and support your reports in understanding, and pursuing, their professional growth
  • Maintain a deep understanding of the team's technical work and its implications for AI safety

You may be a good fit if you:

  • Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
  • Have a background in machine learning, AI, or a similar related technical field
  • Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
  • Excel at building strong relationships with stakeholders at all levels
  • Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
  • Have experience managing teams through periods of rapid growth and change
  • Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you'll need to understand (at a high level of abstraction) to be effective

Strong candidates may also have experience with:

  • High performance, large-scale ML systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

Logistics

  • Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
  • Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
  • Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic, we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave policies, and a dynamic and inclusive work environment.

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]]> full-time mid hybrid $425,000 - $560,000USD machine learning, AI, performance systems, distributed systems, high performance, large-scale ML systems, GPU/Accelerator programming, ML framework internals, OS internals, language modeling with transformers, high performance, large-scale ML systems, GPU/Accelerator programming, ML framework internals, OS internals, language modeling with transformers Engineering Technology Anthropic https://logos.yubhub.co/anthropic.com.png Anthropic is a public benefit corporation that creates reliable, interpretable, and steerable AI systems. The company has a quickly growing team of researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. https://job-boards.greenhouse.io https://job-boards.greenhouse.io/anthropic/jobs/4741102008 San Francisco, CA | New York City, NY | Seattle, WA 2026-03-08 7badeaf5-492 Hardware / Software CoDesign Engineer Hardware / Software CoDesign Engineer

Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Scaling

Compensation

  • $342K – $555K • Offers Equity

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

Benefits

  • Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
  • Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
  • 401(k) retirement plan with employer match
  • Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
  • Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
  • 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
  • Mental health and wellness support
  • Employer-paid basic life and disability coverage
  • Annual learning and development stipend to fuel your professional growth
  • Daily meals in our offices, and meal delivery credits as eligible
  • Relocation support for eligible employees
  • Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI.

About the Role

As an Engineer on our hardware optimization and co-design team, you will co-design future hardware from different vendors for programmability and performance. You will work with our kernel, compiler and machine learning engineers to understand their unique needs related to ML techniques, algorithms, numerical approximations, programming expressivity, and compiler optimizations. You will evangelize these constraints with various vendors to develop and influence future hardware architectures towards efficient training and inference on our models. If you are excited about efficiently distributing a large language model across devices, dealing with and optimizing system-wide/rack-wide networking bottlenecks and eventually tailoring the compute pipe and memory hierarchy of the hardware platform, simulating workloads at different abstractions and working closely with our partners, this is the perfect opportunity!

In this role, you will:

  • Co-design future hardware for programmability and performance with our hardware vendors
  • Assist hardware vendors in developing optimal kernels and add support for it in our compiler
  • Develop performance estimates for critical kernels for different hardware configurations and drive decisions on compute core and memory hierarchy features
  • Build system performance models at different abstraction levels and carry out analysis to drive decisions on scale up, scale out, front end networking
  • Work with machine learning engineers, kernel engineers and compiler developers to understand their vision and needs from high performance accelerators
  • Manage communication and coordination with internal and external partners
  • Influence the roadmap of hardware partners to optimize them for OpenAI’s workloads.
  • Evaluate potential partners’ accelerators and platforms.
  • As the scope of the role and team grows, understand and influence roadmaps for hardware partners for our datacenter networks, racks, and buildings.

You might thrive in this role if you have:

  • 4+ years of industry experience, including experience harnessing compute at scale and optimizing ML platform code to run efficiently on target hardware.
  • Strong experience in software/hardware co-design
  • Deep understanding of GPU and/or other AI accelerators
  • Experience with CUDA, Triton or a related accelerator programming language
  • Experience driving Machine Learning accuracy with low precision formats
  • Experience with system performance modeling and analysis to optimize ML model deployment
  • Strong coding skills in C/C++ and Python
  • Are familiar with the fundamentals of deep learning computing and chip architecture/microarchitecture.

These attributes are nice to have:

  • PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing. Compilers or other Systems
  • Strong understanding of LLMs and challenges related to their training and inference

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]]> full-time senior hybrid $342K – $555K • Offers Equity software/hardware co-design, GPU and/or other AI accelerators, CUDA, Triton or a related accelerator programming language, Machine Learning accuracy with low precision formats, system performance modeling and analysis to optimize ML model deployment, C/C++ and Python, PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing. Compilers or other Systems, Strong understanding of LLMs and challenges related to their training and inference Engineering Technology OpenAI https://logos.yubhub.co/openai.com.png OpenAI is a technology company that develops and commercializes advanced artificial intelligence (AI) systems. The company was founded in 2015 and is headquartered in San Francisco, California. https://jobs.ashbyhq.com https://jobs.ashbyhq.com/openai/bdbb2292-ecb3-42dc-ba89-65edf397d8f8 San Francisco 2026-03-06 35f8645a-02b Software Engineer, Hardware Job Posting

Software Engineer, Hardware

Location

San Francisco

Employment Type

Full time

Department

Scaling

Compensation

  • $266K – $455K • Offers Equity

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

  • Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
  • Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
  • 401(k) retirement plan with employer match
  • Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
  • Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
  • 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
  • Mental health and wellness support
  • Employer-paid basic life and disability coverage
  • Annual learning and development stipend to fuel your professional growth
  • Daily meals in our offices, and meal delivery credits as eligible
  • Relocation support for eligible employees
  • Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI.

About the Role

As a software engineer on the Scaling team, you’ll help build and optimize the low-level stack that orchestrates computation and data movement across OpenAI’s supercomputing clusters. Your work will involve designing high-performance runtimes, building custom kernels, contributing to compiler infrastructure, and developing scalable simulation systems to validate and optimize distributed training workloads.

You will work at the intersection of systems programming, ML infrastructure, and high-performance computing, helping to create both ergonomic developer APIs and highly efficient runtime systems. This means balancing ease of use and introspection with the need for stability and performance on our evolving hardware fleet.

This role is based in San Francisco, CA, with a hybrid work model (3 days/week in-office). Relocation assistance is available.

In this role, you will:

  • Design and build APIs and runtime components to orchestrate computation and data movement across heterogeneous ML workloads.
  • Contribute to compiler infrastructure, including the development of optimizations and compiler passes to support evolving hardware.
  • Engineer and optimize compute and data kernels, ensuring correctness, high performance, and portability across simulation and production environments.
  • Profile and optimize system bottlenecks, especially around I/O, memory hierarchy, and interconnects, at both local and distributed scales.
  • Develop simulation infrastructure to validate runtime behaviors, test training stack changes, and support early-stage hardware and system development.
  • Rapidly deploy runtime and compiler updates to new supercomputing builds in close collaboration with hardware and research teams.
  • Work across a diverse stack, primarily using Rust and Python, with opportunities to influence architecture decisions across the training framework.

You might thrive in this role if you:

  • Have a deep curiosity for how large-scale systems work and enjoy making them faster, simpler, and more reliable.
  • Are proficient in systems programming (e.g., Rust, C++) and scripting languages like Python.
  • Have experience in one or more of the following areas: compiler development, kernel authoring, accelerator programming, runtime systems, distributed systems, or high-performance simulation.
  • Are excited to work in a fast-paced, highly collaborative environment with evolving hardware and ML system demands.
  • Value engineering excellence, technical leadership, and thoughtful system design.

About OpenAI

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

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]]> full-time mid hybrid $266K – $455K • Offers Equity Rust, C++, Python, compiler development, kernel authoring, accelerator programming, runtime systems, distributed systems, high-performance simulation, systems programming, ML infrastructure, high-performance computing Engineering Technology OpenAI https://logos.yubhub.co/openai.com.png OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. https://jobs.ashbyhq.com https://jobs.ashbyhq.com/openai/778e3a4f-c318-4a79-a745-00e722e5ee47 San Francisco 2026-03-06