Research Engineer, Reinforcement Learning

About

• Want to build the large-scale RL environments frontier labs use to train agents that can truly reason and act?
• This team are creating complex reinforcement learning environments - simulations where advanced agents learn to plan, adapt, and solve multi-step problems that stretch beyond standard benchmarks.
• The focus isn't on training the models themselves, but on building the worlds that make meaningful learning and evaluation possible - the foundation for more capable, aligned systems.

Responsibilities

• You'll work end-to-end across environment design, reward dynamics, and scalable simulation - developing the feedback loops that define what "good" looks like for intelligent behaviour.
• It's open-ended, research-driven work where the task definition, data, and reward structure are often the hardest and most important problems to solve.
• You'll collaborate closely with researchers tackling unsolved challenges in reinforcement learning and agent behaviour, shaping experiments, scaling infrastructure, and refining how agents learn in the loop.

Requirements

• It suits someone with strong ML and RL experience, deep intuition for agent dynamics, and the curiosity to explore problems that don't come with clear instructions.

Location

• On-site in San Francisco.

Compensation

• Compensation up to $300 K base (negotiable, depending on experience) plus equity.

Application

• If you want to help build the environments that teach the next generation of AI systems how to think, act, and adapt - we'd love to hear from you.