Research Engineer / Performance Engineer, RL Distributed Systems

Job summary

Responsibilities

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.

• 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

What they're looking 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 across compute, memory, storage, and network
• Experience debugging complex failures across many hosts and services, including failures you can't reproduce locally

Tech stack

• Python
• Rust
• C++
• Kubernetes