Humanoid Locomotion Reinforcement Learning Engineer

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7z3iqx

pb Location: /b Via Melen 83, 16152 Genoa, Italy pb Contract: /b Full-time, Permanent /pp Per essere preso/a in considerazione per un colloquio, la preghiamo di assicurarsi che la sua candidatura sia pienamente in linea con le specifiche del lavoro riportate di seguito. brpb About Us /b /pp 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. /p pb Role /b /pp We are looking for a talented and driven b Humanoid Locomotion Reinforcement Learning Engineer /b 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. /p pb Responsibilities /b /pulli Develop and train reinforcement learning policies for humanoid locomotion, balance control, and whole-body motion; /lili Design motion generation, imitation learning, and motion retargeting pipelines using demonstrations, motion capture data, and reference trajectories; /lili Build and maintain accurate robot, actuator, and contact models using simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent platforms; /lili Develop domain randomization, system identification, and adaptation techniques to improve sim-to-real transfer performance; /lili Deploy, validate,



and optimize learned control policies on physical robots using Python and C++; /lili Implement monitoring, fall detection, recovery strategies, and policy validation mechanisms to ensure safe robot operation; /lili Analyze performance through simulation results, telemetry, robot logs, and experimental testing; /lili Collaborate closely with Mechanical, Electronics, Perception, Controls, and AI teams to integrate locomotion capabilities into the humanoid platform; /li /ul pb Requirements /b /pulli Master’s degree or PhD in Robotics, Control Engineering, Machine Learning, Computer Science, or a related field; /lili Experience developing and applying reinforcement learning techniques to humanoid, legged, or whole-body robotic systems; /lili Strong knowledge of robot kinematics, dynamics, contact modeling, state estimation, and feedback control systems; /lili Experience working with robotics simulation platforms such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent environments; /lili Knowledge of deep reinforcement learning, imitation learning, motion priors, or learning-based control approaches; /lili Strong Python programming skills and practical experience with C++ for real-time robotic applications; /lili Experience with PyTorch or equivalent machine learning frameworks; /lili Experience developing, testing, and debugging software on physical robotic systems; /lili Familiarity with Linux, Git,



and software development best practices; /lili Strong analytical and problem-solving skills, with the ability to work effectively in multidisciplinary teams; /li /ul pb Valued Extras /b /pulli Experience generating, retargeting, blending, and adapting motion priors from motion capture datasets, demonstrations, animation assets, or learned motion datasets; /lili Knowledge of whole-body control, model predictive control (MPC), trajectory optimization, inverse dynamics, or hierarchical control architectures; /lili Experience with sim-to-real methodologies, loco-manipulation, or contact-rich robotic behaviors; xrdbqlu /lili Familiarity with fall prevention, disturbance rejection, recovery strategies, and safe policy execution techniques; /lili Publications in robotics, machine learning, or control systems conferences and journals; /lili Contributions to open-source robotics projects or demonstrated personal robotics projects; /li /ul pb We Offer /b /pulli The opportunity to contribute to the development of cutting-edge humanoid robotic systems; /lili Work on challenging robotics and Physical AI problems with direct real-world impact; /lili A stimulating and informal work environment alongside highly skilled technical and research teams; /lili Employment contract under the Italian Metalworking Collective Labor Agreement (CCNL Metalmeccanico), commensurate with experience; /lili Concrete opportunities for professional growth; /li /ul pb Disclaimer /b /pp We are proud to be an Equal Opportunity Employer. We evaluate all qualified applicants solely on the basis of merit and business needs, without distinction or discrimination based on gender, race, color, ethnic or social origin, age, religion, sexual orientation, gender identity, disability, or any other characteristic protected by law. /p /p /p

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7z3iqx