Research Engineer, Universes
Builds next-generation training environments and evaluations for agentic AI models, blending research in reinforcement learning with robust engineering implementation. Requires strong technical judgment, agency, and experience in ML systems.
About the job
About the Team
The Universes team within Research builds training environments for AI models to perform complex, long-horizon agentic tasks in ultra-realistic settings, teaching models to navigate ambiguity, handle interruptions, maintain context, and exercise judgment.
About the Role
Research Engineers build next-generation training environments for capable and safe agentic AI. This role blends research and engineering, implementing novel approaches, contributing to research direction, reinforcement learning, environment design, and capability evaluations.
Responsibilities
- Build the next generation of agentic environments
- Build rigorous evaluations that measure real capability
- Collaborate across research and infrastructure teams to ship environments into production training
- Debug and iterate rapidly across research and production ML stacks
- Contribute to research culture through technical discussions and collaborative problem-solving
You may be a good fit if you
- Are highly impact-driven — you care about outcomes, not activity
- Operate with high agency
- Have good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces
- Can balance research exploration with engineering implementation
- Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
- Are comfortable with uncertainty and adapt quickly as the landscape shifts
- Have strong software engineering skills and can build robust infrastructure
- Enjoy pair programming (we love to pair!)
Strong candidates may also have
- Industry experience with large language model training, fine-tuning or evaluation
- Industry experience building RL environments, simulation systems, or large-scale ML infrastructure
- Senior experience in a relevant technical field even if transitioning domains
- Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems
- Published influential work in relevant ML areas
Logistics
Education requirements: At least a Bachelor's degree in a related field or equivalent experience.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Skills
Reinforcement Learning, LLMs, Simulation Systems, ML Infrastructure, Python, Distributed Systems, Containerization, Sandboxing, Virtual Machines, Software Engineering
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