Research Engineer / Performance Engineer, RL Distributed Systems

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

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 responsibilities

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 it's 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 run's 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 don't 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

Minimum qualifications

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 can't reproduce locally

Strong written communication, including design documents and incident writeups

Preferred qualifications

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

Representative projects

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 run's bottleneck shifts

Build a diagnostics system that explains why a run's 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 can't recur

Design the operational interface for a run so that automated tools can safely diagnose and adjust it under human oversight

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:

$500,000 — $850,000 USD

Logistics

Minimum education: Bachelor’s 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 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 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-sca