Reinforcement Learning Researcher

What you'll do

• Set research questions and lead offline reinforcement learning studies through communicating findings.
• Improve learned control policies and train models on large, uneven-quality datasets using value estimation approaches.
• Investigate model-based methods, continuous control, and training stability at scale; design rigorous experiments and baselines.

What they ask for

• Research or applied scientist background in reinforcement learning connected to generative visual models.
• Published papers accepted at leading machine learning or robotics venues.
• Expertise in core and offline reinforcement learning topics, including value estimation, goal-directed learning, policy design, and training stability at scale.
• Advanced degree (PhD or master's) in machine learning, computer science, or an adjacent area, centered on reinforcement learning; ability to work on-site six days a week.

Nice to have

• Prior exposure to visual generative models applied to robotics; research experience at a top-tier AI organization.
• Contributions to publicly shared reinforcement learning codebases; experience with large-scale simulation, sim-to-real transfer, or robotics, including JAX.

Pay

USD 200,000 to 350,000 per year (base salary)

Tools

JAX

AI focus

Research in offline reinforcement learning, learned control policies, value estimation, model-based continuous control, generative visual models, and training stability at scale.