Anthropic
Research Engineer / Performance Engineer, RL Distributed Systems
$500K–$850K
San Francisco, CA +3 · Full-time · Posted Sep 29, 2026
- Location
- San Francisco, CA
New York City, NY
Seattle, WA
San Francisco, California, United States - Experience
- Years of experience required will correlate with the internal job level requirements for the position.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; required field of study is a field relevant to the role as demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not for every role or candidate; if an offer is made, Anthropic will make every reasonable effort to obtain a visa and provide an immigration lawyer.
About the job
Design, build, and operate distributed systems that run reinforcement learning workloads at scale, improving performance, reliability, and observability.
What you'll do
- Build and operate systems for RL training, sampling, and environment execution, removing bottlenecks across scheduling, data movement, storage, and networking.
- Develop fault tolerance, resource management, autoscaling, and observability so long-running jobs progress and can be safely operated.
- Preserve training correctness through testing, incident review, system redesign, and collaboration with researchers and performance engineers.
What they ask for
- 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, and debug complex failures across many hosts and services.
Nice to have
- Experience running ML training or inference infrastructure at scale.
- Experience with Kubernetes, sandboxed or virtualized code execution, high-performance networking, observability, or automated remediation.
Pay
500,000—850,000 USD (base salary)
Tools
Python, Rust, C++, Go, Kubernetes, Trio, asyncio, RDMA
AI focus
Build distributed infrastructure for reinforcement learning workloads that support how Claude learns to reason, write code, and act autonomously.
Model-written summary · the original posting is authoritative