Reinforcement Learning Engineer - Locomanipulation

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.

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-behavior policy learning.

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  Design and train reinforcement learning policies for humanoid robot control.

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  Build scalable simulation and training pipelines (e.g., Isaac Lab, MuJoCo).

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  Design reward functions, observation spaces, and curricula for complex behaviors.

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  Improve robustness and sim-to-real transfer of learned policies.

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  Deploy and evaluate policies on real robotic systems.

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  Integrate policies into the control stack.

• MS or PhD in Robotics, Machine Learning, Computer Science, or related field.

• Strong experience with reinforcement learning (e.g., PPO, SAC, offline RL).

• Experience applying RL to robotics or physical systems.

• Experience deploying learned policies on real robotic systems.

• Experience with physics-based simulation environments (e.g., Isaac Lab, MuJoCo).

• Strong programming skills in Python and/or C++.

• Experience with RL for locomotion or legged robots.

• Experience with sim-to-real transfer.

• Familiarity with robot dynamics, control, or whole-body control.