Humanoid Locomotion Reinforcement Learning Engineer

Location: Via Melen 83, 16152 Genoa, Italy

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 Science, or a related field;
• Experience developing and applying reinforcement learning techniques to humanoid, legged, or whole-body robotic systems;
• Strong knowledge of robot kinematics, dynamics, contact modeling, state estimation, and feedback control systems;
• Experience working with robotics simulation platforms such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent environments;
• Knowledge of deep reinforcement learning, imitation learning, motion priors, or learning-based control approaches;
• Strong Python programming skills and practical experience with C++ for real-time robotic applications;
• Experience withPyTorch or equivalent machine learning frameworks;
• Experience developing, testing, and debugging software on physical robotic systems;
• Familiarity with Linux, Git, and software development best practices;
• Strong analytical and problem-solving skills, with the ability to work effectively in multidisciplinary teams;

Valued Extras

• Experience generating, retargeting, blending, and adapting motion priors from motion capture datasets, demonstrations, animation assets, or learned motion datasets;
• Knowledge of whole-body control, model predictive control (MPC), trajectory optimization, inverse dynamics, or hierarchical control architectures;
• Experience withsim-to-real methodologies, loco-manipulation, or contact-rich robotic behaviors;
• Familiarity with fall prevention, disturbance rejection, recovery strategies, and safe policy execution techniques;
• Publications inrobotics, machine learning, or control systems conferences and journals;
• Contributions to open-source robotics projects or demonstrated personal robotics projects;

We Offer

• The opportunity to contribute to the development of cutting-edge humanoid robotic systems;
• Work on challenging robotics and Physical AI problems with direct real-world impact;
• A stimulating and informal work environment alongside highly skilled technical and research teams;
• Employment contract under the Italian Metalworking Collective Labor Agreement (CCNL Metalmeccanico), commensurate with experience;
• Concrete opportunities for professional growth;

Disclaim