Humanoid Locomotion RL Engineer

Generative Bionics; Genova; In loco; EUR 60.000 - 90.000; Tempo pieno

Vantaggi offerti da questo lavoro

CCNL Metalmeccanico contract

Professional growth opportunities

Equal opportunity employer

Descrizione del lavoro

Generative Bionics S.r.l. in Genova, Italy, seeks a Humanoid Locomotion & Reinforcement Learning Engineer to advance locomotion and whole-body motion for our humanoid robot platform.

You will work at the intersection of robotics, ML, and control, designing RL-based solutions and contributing to simulation, policy training, and real-world deployment. The role requires expertise in RL for legged systems, kinematics, and real-time control, with strong Python/C++ skills and experience with PyTorch.

Competenze

• Advanced degree in a robotics, ML, or control field.
• Proven RL experience on humanoid or legged systems.
• Strong skills in Python and C++ for real-time robotics.
• Experience with simulation to real deployment pipelines.

Mansioni

• Develop and train reinforcement learning policies for humanoid locomotion and whole-body motion.
• Design motion generation, imitation learning, and retargeting pipelines.
• Build and maintain robot models and contact dynamics in simulators.
• Improve sim-to-real transfer with domain randomization and system identification.
• Deploy and optimize policies on physical robots using Python and C++.
• Collaborate with Mechanical, Electronics, Perception, Controls, and AI teams.

Conoscenze

Python

C++

Reinforcement learning

Robotics

Kinematics

Control theory

PyTorch

Team collaboration

Formazione

Master’s degree or PhD in Robotics, Control Engineering, ML, CS

Strumenti

Isaac Lab

Isaac Sim

MuJoCo

Descrizione del lavoro

Generative Bionics S.r.l. in Genova, Italy, seeks a Humanoid Locomotion & Reinforcement Learning Engineer to advance locomotion and whole-body motion for our humanoid robot platform.

You will work at the intersection of robotics, ML, and control, designing RL-based solutions and contributing to simulation, policy training, and real-world deployment. The role requires expertise in RL for legged systems, kinematics, and real-time control, with strong Python/C++ skills and experience with PyTorch.