Research Engineer, Machine Learning (Reinforcement Learning)
San Francisco, CA | New York City, NY
Posted 16d ago
About the job
As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.
Responsibilities
- Architect and optimize core reinforcement learning infrastructure, including training abstractions and distributed experiment management.
- Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents.
- Drive performance improvements across the technology stack through profiling, optimization, and benchmarking.
- Implement efficient caching solutions and debug distributed systems.
- Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure.
Requirements
- Proficient in Python and async/concurrent programming with frameworks like Trio.
- Experience with machine learning frameworks (PyTorch, TensorFlow, JAX).
- Industry experience in machine learning research.
- Ability to balance research exploration with engineering implementation.
- Strong systems design and communication skills.
- Passion for the potential impact of AI and commitment to developing safe and beneficial systems.
- Familiarity with LLM architectures and training methodologies.
- Experience with reinforcement learning techniques and environments.
- Experience with virtualization and sandboxed code execution environments.
- Experience with Kubernetes.
- Experience with distributed systems or high-performance computing.
- Experience with Rust and/or C++.