Humanoid Locomotion Reinforcement Learning Engineer
Generative Bionics ·
ppbContract: /bFull-time, Permanent /p h3About Us /h3 pGenerative 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 h3Role /h3 pWe 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 h3Responsibilities /h3 ul liDevelop and train reinforcement learning policies for humanoid locomotion, balance control, and whole-body motion; /li liDesign motion generation, imitation learning, and motion retargeting pipelines using demonstrations, motion capture data, and reference trajectories; /li liBuild and maintain accurate robot, actuator, and contact models using simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent platforms; /li liDevelop domain randomization, system identification, and adaptation techniques to improve sim-to-real transfer performance; /li liDeploy, validate, and optimize learned control policies on physical robots using Python and C++; /li liImplement monitoring, fall detection, recovery strategies, and policy validation mechanisms to ensure safe robot operation; /li liAnalyze performance through simulation results, telemetry, robot logs, and experimental testing; /li liCollaborate closely with Mechanical, Electronics, Perception, Controls, and AI teams to integrate locomotion capabilities into the humanoid platform; /li /ul h3Requirements /h3 ul liMaster’s degree or PhD in Robotics, Control Engineering, Machine Learning, Computer Science, or a related field; /li liExperience developing and applying reinforcement learning techniques to humanoid, legged, or whole-body robotic systems; /li liStrong knowledge of robot kinematics, dynamics, contact modeling, state estimation, and feedback control systems; /li liExperience working with robotics simulation platforms such as Isaac Lab, Isaac Sim, MuJoCo, or equivalent environments; /li liKnowledge of deep reinforcement learning, imitation learning, motion priors, or learning-based control approaches; /li liStrong Python programming skills and practical experience with C++ for real-time robotic applications; /li liExperience with PyTorch or equivalent machine learning frameworks; /li liExperience developing, testing, and debugging software on physical robotic systems; /li liFamiliarity with Linux, Git, and software development best practices; /li liStrong analytical and problem-solving skills, with the ability to work effectively in multidisciplinary teams; /li liExperience generating, retargeting, blending, and adapting motion priors from motion capture datasets, demonstrations, animation assets, or learned motion datasets; /li liKnowledge of whole-body control, model predictive control (MPC), trajectory optimization, inverse dynamics, or hierarchical control architectures; /li liExperience with sim-to-real methodologies, loco-manipulation, or contact-rich robotic behaviors; /li liFamiliarity with fall prevention, disturbance rejection, recovery strategies, and safe policy execution techniques; /li liPublications in robotics, machine learning, or control systems conferences and journals; /li liContributions to open-source robotics projects or demonstrated personal robotics projects; /li /ul h3We Offer /h3 ul liThe opportunity to contribute to the development of cutting-edge humanoid robotic systems; /li liWork on challenging robotics and Physical AI problems with direct real-world impact; /li liA stimulating and informal work environment alongside highly skilled technical and research teams; /li liEmployment contract under the Italian Metalworking Collective Labor Agreement (CCNL Metalmeccanico), commensurate with experience; /li liConcrete opportunities for professional growth; /li /ul h3Disclaimer /h3 pWe 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