Research Engineer, Universes

About the Team

The Universes team within Research is responsible for training AI models to perform complex, difficult, long-horizon agentic tasks in ultra-realistic settings. We design and implement novel training environments that go far beyond what models can do today — environments where models learn to navigate ambiguity, handle interruptions, maintain context over extended interactions, and exercise judgment in open-ended scenarios.

About the Role

We're looking for Research Engineers to help us build the next generation of training environments for capable and safe agentic AI.

This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to research direction. You'll work on fundamental research in reinforcement learning, designing training environments and methodologies that push the state of the art, and building evaluations that measure genuine capability.

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 one or more of the following:

• Have industry experience with large language model training, fine-tuning or evaluation

• Have 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