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.
What you'll do
- Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters.
- Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
- Drive performance improvements across our stack through profiling, optimization, and benchmarking.
- Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.
What you need
- 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
