Research Engineer, Machine Learning (Reinforcement Learning)

Position Overview

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. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Responsibilities

• Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.
• 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. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
• Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.

Requirements & Skills

• 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 (we love to pair!)
• Care about code quality, testing, and performance
• Have strong systems design and communication skills
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
• Familiarity with LLM architectures and training methodologies
• Experience with reinforcement learning techniques and environments
• Experience with virtualization and sandboxed code execution environments
• Experience with Kubernetes
• Experience with distributed systems or high-performance computing
• Experience with Rust and/or C++
• Formal certifications or education credentials (not required)
• Academic research experience or publication history (not required)