Humanoid Locomotion Reinforcement Learning Engineer

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7ulr9a

ppDescrizione dell’offerta di lavoro /p /brpbLocation: /b Via Melen 83, 16152 Genoa, Italy /p /brpbContract: /b Full-time, Permanent /p /brh3About Us /h3 /brpGenerative 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 /brh3Role /h3 /brpWe are looking for a talented and driven bHumanoid 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 /brh3Responsibilities /h3 /brul /brliDevelop and train reinforcement learning policies for humanoid locomotion, balance control, and whole-body motion; /li /brliDesign motion generation, imitation learning, and motion retargeting pipelines using demonstrations, motion capture data, and reference trajectories; /li /brliBuild and maintain accurate robot, actuator, and contact models using simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent platforms; /li /brliDevelop domain randomization, system identification, and adaptation techniques to improve sim-to-real transfer performance; /li /brliDeploy, validate, and optimize learned control policies on physical robots using Python and C++; /li /brliImplement monitoring, fall detection, recovery strategies,



and policy validation mechanisms to ensure safe robot operation; /li /brliAnalyze performance through simulation results, telemetry, robot logs, and experimental testing; /li /brliCollaborate closely with Mechanical, Electronics, Perception, Controls, and AI teams to integrate locomotion capabilities into the humanoid platform; /li /br /ul /brh3Requirements /h3 /brul /brliMaster’s degree or PhD in Robotics, Control Engineering, Machine Learning, Computer Science, or a related field; /li /brliExperience developing and applying reinforcement learning techniques to humanoid, legged, or whole-body robotic systems; /li /brliStrong knowledge of robot kinematics, dynamics, contact modeling, state estimation, and feedback control systems; /li /brliExperience working with robotics simulation platforms such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent environments; /li /brliKnowledge of deep reinforcement learning, imitation learning, motion priors, or learning-based control approaches; /li /brliStrong Python programming skills and practical experience with C++ for real-time robotic applications; /li /brliExperience with PyTorch or equivalent machine learning frameworks; /li /brliExperience developing, testing, and debugging software on physical robotic systems; /li /brliFamiliarity with Linux, Git, and software development best practices; /li /brliStrong analytical and problem-solving skills,



with the ability to work effectively in multidisciplinary teams; /li /br /ul /brh3Valued Extras /h3 /brul /brliExperience generating, retargeting, blending, and adapting motion priors from motion capture datasets, demonstrations, animation assets, or learned motion datasets; /li /brliKnowledge of whole-body control, model predictive control (MPC), trajectory optimization, inverse dynamics, or hierarchical control architectures; /li /brliExperience with sim-to-real methodologies, loco-manipulation, or contact-rich robotic behaviors; /li /brliFamiliarity with fall prevention, disturbance rejection, recovery strategies, and safe policy execution techniques; /li /brliPublications in robotics, machine learning, or control systems conferences and journals; /li /brliContributions to open-source robotics projects or demonstrated personal robotics projects; /li /br /ul /brh3We Offer /h3 /brul /brliThe opportunity to contribute to the development of cutting-edge humanoid robotic systems; /li /brliWork on challenging robotics and Physical AI problems with direct real-world impact; /li /brliA stimulating and informal work environment alongside highly skilled technical and research teams; /li /brliEmployment contract under the Italian Metalworking Collective Labor Agreement (CCNL Metalmeccanico), commensurate with experience; /li /brliConcrete opportunities for professional growth; /li /br /ul /brh3Disclaimer /h3 /brpWe 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

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7ulr9a