Reinforcement Learning Engineer - Locomanipulation

We are looking for a Senior or Staff Reinforcement Learning Engineer to develop learning-based control policies for humanoid robots.

You will design and train reinforcement learning policies that enable dynamic locomotion and loco-manipulation behaviors on real robots. Your work will focus on building scalable training pipelines, designing reward functions and environments, and improving sim-to-real transfer for reliable deployment on hardware.

You will work closely with controls and robotics engineers to integrate learned policies into the robot control stack, ensuring stable and robust behavior in real-world conditions.

Development will involve continuous iteration between large-scale simulation and hardware experiments.