Research Engineer, Machine Learning (Reinforcement Learning)

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.