Research Engineer / Performance Engineer, RL Distributed Systems (AI)

Location: San Francisco, CA; New York City, NY; or Seattle, WA. Staff are expected to work from one of the offices at least 25% of the time.

Annual salary: $500,000–$850,000 USD

Company

Anthropic is a public benefit corporation building reliable, interpretable, and steerable AI systems.

What you will do

• Design, build, and operate distributed systems for reinforcement learning across training, sampling, and environment execution.
• Improve scheduling, data movement, storage, networking, coordination, resource management, and autoscaling.
• Build fault detection, isolation, recovery, observability, and automated remediation for large-scale workloads.
• Develop safe operational interfaces for engineers and automated tools to diagnose and adjust runs.
• Work with researchers and performance engineers to preserve training correctness and prevent nondeterminism.
• Use incident reviews, testing, redesign, and technical documentation to eliminate recurring failure modes.

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 consistency, coordination, consensus, failure modes, and recovery.
• Ability to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and networking.
• Experience debugging complex failures across many hosts and services, including failures that cannot be reproduced locally.
• Bachelor’s degree or equivalent education, training, or experience in a relevant field.

Nice to have

• Experience with ML training or inference infrastructure, schedulers, autoscalers, resource management, Kubernetes, or sandboxed and virtualized code execution.
• Experience with high-performance networking, RDMA, collective communication libraries, observability, automated remediation, or async Python frameworks such as Trio or asyncio.
• Familiarity with reinforcement learning or large language model training workloads.

Culture & Benefits

• Collaborative research environment focused on large-scale AI research and trustworthy AI.
• Flexible working hours and an office environment designed for collaboration.
• Generous vacation and parental leave.
• Competitive compensation and optional equity donation matching.
• Visa sponsorship is available, although sponsorship cannot be guaranteed for every role or candidate.