About the company
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.
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.
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
- Design resource management and autoscaling so that compute follows demand
- Build observability that makes it possible to understand what a run is doing and why it slowed down
- Build automation that detects and remediates common problems
- Work with researchers and performance engineers to make sure systems changes preserve training correctness
- Remove classes of failure at their source through incident review, testing, and redesign
Requirements
- 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
- 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
Conditions
- Annual Salary: $500,000 - $850,000 USD
- Location-based hybrid policy: all staff expected in one of our offices at least 25% of the time
- Visa sponsorship available
- Minimum education: Bachelor's degree or equivalent