Research Engineer, Machine Learning (Reinforcement Learning)

Overview

Research Engineer in Reinforcement Learning to advance the capabilities and safety of large language models by implementing novel approaches and contributing to research direction.

What you'll do

• Implement novel reinforcement learning approaches for large language models.
• Create agentic models via tool use for open-ended tasks like computer use and autonomous software generation.
• Improve reasoning abilities, including in mathematics.
• Architect and optimize core RL infrastructure, including distributed experiment management.
• Design, implement, and test training environments, evaluations, and methodologies.
• Drive performance improvements through profiling, optimization, and benchmarking.
• Build scalable infrastructure including automated testing frameworks and clean APIs.

What you'll need

• Proficiency in Python and async/concurrent programming with frameworks like Trio.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
• Industry experience in machine learning research.
• Ability to balance research exploration with engineering implementation.
• Commitment to code quality, testing, and performance.
• Strong systems design and communication skills.
• Pair programming experience/preference (they “love to pair”).

Details

• Location-based hybrid policy: expected to be in an office at least 25% of the time.
• Visa sponsorship is available in some cases.
• Annual compensation range: $500,000–$850,000 USD.