Research Engineer, Machine Learning (Reinforcement Learning)
Research Engineer builds and optimizes reinforcement learning infrastructure for advancing AI capabilities like agentic models, tool use, and reasoning. Requires Python proficiency, ML frameworks experience, and ability to blend research with scalable engineering.
About the job
Representative Projects
- Architect and optimize core reinforcement learning infrastructure, from training abstractions to distributed experiment management across GPU clusters.
- Design, implement, and test novel training environments, evaluations, and methodologies for RL agents.
- Drive performance improvements through profiling, optimization, benchmarking, efficient caching, and debugging distributed systems.
- Collaborate to develop automated testing frameworks, clean APIs, and scalable infrastructure.
You May Be a Good Fit If You
- Are proficient in Python and async/concurrent programming with frameworks like Trio.
- Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX).
- Have industry experience in machine learning research.
- Can balance research exploration with engineering implementation.
- Enjoy pair programming.
- Care about code quality, testing, and performance.
- Have strong systems design and communication skills.
- Are passionate about safe and beneficial AI.
Strong Candidates May Have
- Familiarity with LLM architectures and training methodologies.
- Experience with reinforcement learning techniques and environments.
- Experience with virtualization and sandboxed code execution.
- Experience with Kubernetes.
- Experience with distributed systems or high-performance computing.
- Experience with Rust and/or C++.
Strong Candidates Need Not Have
- Formal certifications or education credentials.
- Academic research experience or publication history.
Skills
Python, PyTorch, TensorFlow, JAX, Reinforcement Learning, Kubernetes, Rust, C++, Trio, Distributed Systems
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