Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.

About The Role

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.

The problems you will work on include dynamic locomotion, balance recovery, contact-rich manipulation, and multi-behavi