Research Engineer Performance Engineer, RL Distributed Systems

Anthropics 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 youll 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. Were looking for generalists: engineers who can move between these layers reason from first principles about a system they havent 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.

• Design build and operate the distributed systems that run RL at scale across training sampling and environment execution
• Find and remove whatever currently limits the system whether its scheduling data movement storage networking or coordination
• Build fault tolerance into every layer: failure detection isolation and recovery that keep long-running jobs making progress without human intervention
• Design resource management and autoscaling so that compute follows demand as a runs needs shift
• Build observability that makes it possible to understand what a run is doing and why it slowed down stalled or produced unexpected results
• Build automation that detects and remediates common problems and design interfaces that let engineers and automated tools operate runs safely
• Work with researchers and performance engineers to make sure systems changes preserve training correctness and dont introduce subtle nondeterminism
• Remove classes of failure at their source through incident review testing and redesign and write clear design documents for what you build

• Strong software engineering skills in Python and at least one systems language such as Rust C or Go
• Experience designing building and operating large-scale distributed systems in production
• Deep understanding of distributed systems fundamentals including consistency coordination consensus failure modes and recovery
• Ability to reason quantitatively about throughput latency and resource costs across compute memory storage and network
• Experience debugging complex failures across many hosts and services including failures you cant reproduce locally
• Strong written communication including design documents and incident writeups

• Experience running ML training or inference infrastructure at scale
• Experience across several layers of the stack such as scheduling storage networking and orchestration
• Experience building schedulers autoscalers or resource management systems
• Experience with container orchestration such as Kubernetes and with sandboxed or virtualized code execution at scale
• Experience with high-performance networking RDMA or collective communication libraries
• Experience building observability or automated remediation for large fleets
• Experience with async Python frameworks such as Trio or asyncio
• Familiarity with reinforcement learning or large language model training workloads

• Design a scheduler that places training sampling and environment work across a heterogeneous cluster while respecting network topology and failure domains
• Build a failure detection and recovery system that lets a long-running job survive host and network failures with minimal lost work
• Scale environment execution substantially without increasing tail latency for the training step
• Design an autoscaling policy that rebalances compute across components as a runs bottleneck shifts
• Build a diagnostics system that explains why a runs throughput dropped and proposes a fix
• Trace 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 cant recur
• Design the operational interface for a run so that automated tools can safely diagnose and adjust it under human oversight

Minimum education: Bachelors degree or an equivalent combination of education training and/or experience

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

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

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

Visa sponsorship:We do sponsor visas! However we arent 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 youre interested in this work. We think AI systems like the ones were 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 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 youre ever unsure about a communication dont click any linksvisit for confirmed position openings.

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. Were 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.

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 aboutour policy for using AI in our application process.