Research Engineer, Horizons

As a Research Engineer on the Reinforcement Learning Fundamentals team, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models through fundamental research in reinforcement learning, improving reasoning abilities in areas such as code generation and mathematics, and exploring reinforcement learning for agentic / open-ended tasks. Representative projects: Develop and implement novel reinforcement learning techniques to improve the performance and safety of large language models. Create tools and environments for models to interact with, enabling them to perform complex, open-ended tasks. Design and run experiments to enhance models' reasoning capabilities, particularly in code generation and mathematics. You may be a good fit if you: 5+ years of industry-related experience Are proficient in Python and have experience with deep learning frameworks such as PyTorch or Jax Have a strong software engineering background and are interested in working closely with researchers and other engineers Enjoy pair programming (we love to pair!) Care about code quality, testing, and performance Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Strong candidates may also: Have a strong background in machine learning, reinforcement learning, or high performance computing Have experience with virtualization and sandboxed code execution environments Have experience with Kubernetes Have contributed to open-source projects or published research papers in relevant fields Candidates need not have: Formal certifications or education credentials Experience with LLMs or machine learning research before.

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.