Research Engineer / Performance Engineer, RL Distributed Systems

Research Engineer On The Distributed Systems Team Within Rl EngineeringAnthropic'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.Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is an unusually demanding distributed system. Training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea.As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience.Our system changes as fast as the research does, correctness under failure matters as much as throughput, and the best solutions often come from understanding the ML workload well enough to know which guarantees it actually needs.

Strong candidates have built and run large distributed systems, care about getting the details right, and want to apply that experience to a workload that is very large, very heterogeneous, and changing quickly.Key ResponsibilitiesDesign, build, and operate the distributed systems that run RL at scale, across training, sampling, and environment executionFind and remove whatever currently limits the system, whether it's scheduling, data movement, storage, networking, or coordinationBuild fault tolerance into every layer: failure detection, isolation, and recovery that keep long-running jobs making progress without human interventionDesign resource management and autoscaling so that compute follows demand as a run's needs shiftBuild observability that makes it possible to understand what a run is doing and why it slowed down, stalled, or produced unexpected resultsBuild automation that detects and remediates common problems, and design interfaces that let engineers and automated tools operate runs safelyWork with researchers and performance engineers to make sure systems changes preserve training correctness and don't introduce subtle nondeterminismRemove classes of failure at their source through incident review, testing, and redesign, and write clear design documents for what you buildMinimum QualificationsStrong software engineering skills in Python and at least one systems language such as Rust, C++, or GoExperience designing, building, and operating large-scale distributed systems in productionDeep understanding of distributed systems fundamentals, including consistency, coordination, consensus, failure modes, and recoveryAbility to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and networkExperience debugging complex failures across many hosts and services, including failures you can't reproduce locallyStrong written communication, including design documents and incident writeupsPreferred QualificationsExperience running ML training or inference infrastructure at scaleExperience across several layers of the stack, such as scheduling, storage, networking, and orchestrationExperience building schedulers, autoscalers, or resource management systemsExperience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scaleExperience with high-performance networking, RDMA, or collective communication librariesExperience building observability or automated remediation for large fleetsExperience with async Python frameworks such as Trio or asyncioFamiliarity with reinforcement learning or large language model training workloadsRepresentative ProjectsDesign a scheduler that places training, sampling, and environment work across a heterogeneous cluster while respecting network topology and failure domainsBuild a failure detection and recovery system that lets a long-running job survive host and network failures with minimal lost workScale environment execution substantially without increasing tail latency for the training stepDesign an autoscaling policy that rebalances compute across components as a run's bottleneck shiftsBuild a diagnostics system that explains why a run's throughput dropped and proposes a fixTrace a rare data corruption bug across many services to a race condition in a recovery path, and redesign the path so the class of bug can't recurDesign the operational interface for a run so that automated tools can safely diagnose and adjust it under human oversightThe annual compensation range for this role is listed below.Annual Salary:$500,000 —$850,000 USDLogisticsMinimum education: Bachelor's degree or an equivalent combination of education, training, and/or experienceRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experienceMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the positionLocation-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 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 DifferentWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science.

We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.Come Work With Us!Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.