The opportunity
Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is a large distributed system: training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts.
What you'll do
Design, build, and operate security controls for RL training systems and the infrastructure they run on
Work across the stack (orchestration, environments, training, inference) wherever the work is needed
Review designs and code across the RL stack, find security problems, and fix them
Build shared libraries and tooling that other RL engineers and researchers build on
Build the observability needed to understand what these systems are doing
Work with researchers and performance engineers to make sure security changes: preserve training correctness and performance
What they're looking for
- Experience running ML training or inference infrastructure at scale
- Experience with infrastructure security, such as isolation, access control, networking, or identity
- Experience with container orchestration such as Kubernetes, and with: sandboxed or virtualized code execution at scale
- Experience building observability for large fleets
- Experience supporting research teams or other fast-moving internal users
- Familiarity with reinforcement learning or large language model training workloads