Research Engineer / Performance Engineer, RL Distributed Systems

San Francisco, CA | New York City, NY | Seattle, WA · San Francisco · USD 500,000-850,000 / year · Hybrid · Posted Sep 29, 2026

At a glance

Workplace: Hybrid
Location: San Francisco, CA | New York City, NY | Seattle, WA · San Francisco
Salary: USD 500,000-850,000 / year · Base salary
Visa sponsorship: Visa sponsorship available

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.

As a Research Engineer on the Distributed Systems team within RL Engineering, you will work on whatever part of the system is the current limit, from scheduling and placement to data movement and storage.
Your work ensures that RL runs—which involve concurrent training, sampling, and environment execution across a large fleet of accelerators—keep making progress while hardware fails and load shifts.

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

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
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

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

Location-based hybrid policy requiring staff to be in one of the offices at least 25% of the time, though some roles may require more time in-office
Offices located in San Francisco, CA, New York City, NY, and Seattle, WA