Overview
Research Engineer in Reinforcement Learning to advance the capabilities and safety of large language models by combining reinforcement learning research with engineering implementation.
What you'll do
- Collaborate with researchers and engineers to advance LLM capabilities and safety.
- Implement novel reinforcement learning approaches and contribute to research direction.
- Architect and optimize core reinforcement learning infrastructure, including distributed experiment management.
- Design, implement, and test training environments, evaluations, and methodologies for RL agents.
- Drive performance improvements via profiling, optimization, and benchmarking.
- Implement caching solutions and debug distributed systems for faster training and evaluation.
- Develop automated testing frameworks, clean APIs, and scalable infrastructure for AI research.
What you'll need
- Proficiency in Python and async/concurrent programming with frameworks like Trio.
- Experience with machine learning frameworks including PyTorch, TensorFlow, or JAX.
- Industry experience in machine learning research.
- Ability to balance research exploration with engineering implementation.
- Enjoy pair programming and value code quality, testing, and performance.
- Strong systems design and communication skills.
- Passion for safe and beneficial AI system development.
Details
- Hybrid work policy: expected to be in an office at least 25% of the time.
- Visa sponsorship: sponsored in some cases; efforts made if offered.
Read the full description and apply on the company’s own careers page.
