Humanoid Locomotion Reinforcement Learning Engineer

Location: San Donnino di Liguria

Location:

Via Melen 83, 16152 Genoa, Italy

Contract:

Full-time, Permanent

Per essere preso/a in considerazione per un colloquio, la preghiamo di assicurarsi che la sua candidatura sia pienamente in linea con le specific he del lavoro riportate di seguito.

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, Mu Jo Co , 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, Mu Jo Co , 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 with PyTorch 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 with sim-to-real methodologies, loco-manipulation, or contact-rich robotic behaviors; xjncmbx
Familiarity with fall prevention, disturbance rejection, recovery strategies, and safe policy execution techniques;
Publications in robotics, machine learning, or control systems conferences and journals;
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