Anthropic
Research Engineer, Machine Learning (Reinforcement Learning)
$500K–$850K
San Francisco, CA +3 · Full-time · Posted Apr 1, 2025
- Location
- San Francisco, CA
New York City, NY
New York, New York, United States
San Francisco, California, United States - Experience
- Industry experience in machine learning research; years required correlate with internal job level requirements.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience. Relevant field demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not guaranteed for every role or candidate; if an offer is made, the company will make every reasonable effort to obtain a visa.
About the job
Develop reinforcement learning research and engineering for large language models, including agentic capabilities, reasoning, training infrastructure, evaluations, and scalable research systems.
What you'll do
- Architect and optimize reinforcement learning infrastructure and distributed experiment management across GPU clusters.
- Design and test training environments, evaluations, and methodologies for reinforcement learning agents.
- Improve performance through profiling, optimization, benchmarking, caching, and debugging distributed systems.
What they ask for
- Proficiency in Python and async/concurrent programming, with frameworks such as Trio.
- Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
- Industry experience in machine learning research.
- Strong systems design and communication skills; ability to balance research exploration with engineering implementation.
Nice to have
- Familiarity with LLM architectures and training methodologies, and experience with reinforcement learning techniques and environments.
- Experience with virtualization or sandboxed code execution, Kubernetes, distributed systems or high-performance computing, Rust, or C++.
Pay
$500,000 — $850,000 USD (annual salary; base salary)
Tools
Python, Trio, PyTorch, TensorFlow, JAX, Kubernetes, Rust, C++
AI focus
Research and engineering for reinforcement learning in large language models, including tool-using agents for computer use and autonomous software generation, improved reasoning, and safe, beneficial AI systems.
Model-written summary · the original posting is authoritative