ph3Humanoid Locomotion Reinforcement Learning Engineer /h3h3About Us /h3pGenerative 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. /ph3Role /h3pWe 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. /ph3Responsibilities /h3ulliDevelop and train reinforcement learning policies for humanoid locomotion, balance control, and whole-body motion; /liliDesign motion generation, imitation learning, and motion retargeting pipelines using demonstrations, motion capture data, and reference trajectories; /liliBuild and maintain accurate robot, actuator, and contact models using simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent platforms; /liliDevelop domain randomization, system identification, and adaptation techniques to improve sim-to-real transfer performance; /liliDeploy, validate, and optimize learned control policies on physical robots using Python and C++; /liliImplement monitoring, fall detection, recovery strategies,



and policy validation mechanisms to ensure safe robot operation; /liliAnalyze performance through simulation results, telemetry, robot logs, and experimental testing; /liliCollaborate closely with Mechanical, Electronics, Perception, Controls, and AI teams to integrate locomotion capabilities into the humanoid platform; /li /ulh3Requirements /h3ulliMaster’s degree or PhD in Robotics, Control Engineering, Machine Learning, Computer Science, or a related field; /liliExperience developing and applying reinforcement learning techniques to humanoid, legged, or whole-body robotic systems; /liliStrong knowledge of robot kinematics, dynamics, contact modeling, state estimation, and feedback control systems; /liliExperience working with robotics simulation platforms such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent environments; /liliKnowledge of deep reinforcement learning, imitation learning, motion priors, or learning-based control approaches; /liliStrong Python programming skills and practical experience with C++ for real-time robotic applications; /liliExperience with PyTorch or equivalent machine learning frameworks; /liliExperience developing, testing, and debugging software on physical robotic systems; /liliFamiliarity with Linux, Git, and software development best practices; /liliStrong analytical and problem-solving skills,



with the ability to work effectively in multidisciplinary teams; /li /ululliExperience generating, retargeting, blending, and adapting motion priors from motion capture datasets, demonstrations, animation assets, or learned motion datasets; /liliKnowledge of whole-body control, model predictive control (MPC), trajectory optimization, inverse dynamics, or hierarchical control architectures; /liliExperience with sim-to-real methodologies, loco-manipulation, or contact-rich robotic behaviors; /liliFamiliarity with fall prevention, disturbance rejection, recovery strategies, and safe policy execution techniques; /liliPublications in robotics, machine learning, or control systems conferences and journals; /liliContributions to open-source robotics projects or demonstrated personal robotics projects; /li /ulh3We Offer /h3ulliThe opportunity to contribute to the development of cutting-edge humanoid robotic systems; /liliWork on challenging robotics and Physical AI problems with direct real-world impact; /liliA stimulating and informal work environment alongside highly skilled technical and research teams; /liliEmployment contract under the Italian Metalworking Collective Labor Agreement (CCNL Metalmeccanico), commensurate with experience; /liliConcrete opportunities for professional growth; /li /ulh3Disclaimer /h3pWe 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 #J-18808-Ljbffr

📌 Humanoid Locomotion Reinforcement Learning Engineer (Genova)
🏢 Generative Bionics
📍 Genova