Humanoid Locomotion Reinforcement Learning Engineer

Generative Bionics; Genova; In loco; EUR 70.000 - 95.000; Tempo pieno

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Descrizione del lavoro

Generative Bionics seeks a talented Humanoid Locomotion & Reinforcement Learning Engineer to advance locomotion and whole-body motion for its humanoid robot platform. You will develop RL-based solutions across simulation, policy training, sim-to-real transfer, and deployment on physical robots.

You will work at the intersection of robotics, ML, and control systems, collaborating with multidisciplinary teams to create robust, adaptive robot behavior in real-world environments in Genova, Italy.

Competenze

• Master’s or PhD in Robotics, Control Engineering, ML, CS or related field.
• Experience applying RL to humanoid or legged robots.
• Strong knowledge of kinematics, dynamics, and control systems.
• Experience with robotics simulators such as MuJoCo, Isaac Lab/Sim.

Mansioni

• Develop RL policies for humanoid locomotion and whole-body motion.
• Design motion generation and imitation pipelines from demonstrations and trajectories.
• Train and validate policies in simulation environments and transfer to real robots.
• Collaborate with Mechanical, Electronics, Perception, Controls, and AI teams.

Conoscenze

Reinforcement learning

Robotics

Python

C++

PyTorch

Linux

ROS

Kinematics

Formazione

Master’s or PhD in Robotics/ML

Strumenti

MuJoCo

Isaac Lab

Isaac Sim

Git

ROS

Descrizione del lavoro

Contract: Full-time, Permanent

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, MuJoCo, 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, MuJoCo, 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;
• 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;
• Familiarity with fall prevention, disturbance rejection, recovery strategies, and safe policy execution techniques;
• Publications in robotics, machine learning, or control systems conferences and journals;
• Contributions to open-source robotics projects or demonstrated personal robotics projects;

We Offer

• The opportunity to contribute to the development of cutting-edge humanoid robotic systems;
• Work on challenging robotics and Physical AI problems with direct real-world impact;
• A stimulating and informal work environment alongside highly skilled technical and research teams;
• Employment contract under the Italian Metalworking Collective Labor Agreement (CCNL Metalmeccanico), commensurate with experience;
• Concrete opportunities for professional growth;

Disclaimer

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.