Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems. Backed by Accel, Bessemer and General Catalyst.
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, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. Found on 1752vc Careers, the job board for startup and VC roles.
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
- Strong written communication, including design documents and incident writeups
More about this role
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. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
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, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea.
As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking,...
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