Humanoid Locomotion Reinforcement Learning Engineer
Pubblicato il 22-09-2026 - Generative Bionics in Genova
Contract: Full-time, Permanent
About Us
Generative Bionics is a deep-tech company building humanoid robot platforms to deploy human-centered Physical AI. We design intelligent, capable machines that work alongside people in real-world environments — developed in Genova, Italy.
Role
We are looking for a talented and driven Humanoid Locomotion & Reinforcement Learning Engineer to develop advanced locomotion and whole-body motion capabilities for our humanoid robot platform. In this role, you will work at the intersection of robotics, machine learning, and control systems, designing and deploying reinforcement learning-based solutions that enable robust, dynamic, and adaptive robot behavior. You will contribute to the full development pipeline, from simulation and policy training to sim-to-real transfer and deployment on physical robots.
Responsibilities
- Develop and train reinforcement learning policies for humanoid locomotion, balance control, and whole-body motion;
- Design motion generation, imitation learning, and motion retargeting pipelines using demonstrations,
motion capture data, and reference trajectories;
- Build and maintain accurate robot, actuator, and contact models using simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent platforms;
- Develop domain randomization, system identification, and adaptation techniques to improve sim-to-real transfer performance;
- Deploy, validate, and optimize learned control policies on physical robots using Python and C++;
- Implement monitoring, fall detection, recovery strategies, and policy validation mechanisms to ensure safe robot operation;
- Analyze performance through simulation results, telemetry, robot logs, and experimental testing;
- Collaborate closely with Mechanical, Electronics, Perception, Controls, and AI teams to integrate locomotion capabilities into the humanoid platform;
Requirements
- Master’s degree or PhD in Robotics, Control Engineering, Machine Learning, Computer S