The company
Our client is a leading company in the field of cognitive humanoid robotics based in Monza Brianza and founded in 2020, focused on developing cognitive humanoid robotics for progress and well-being.
The mission is to make the benefits of the future accessible by creating innovative solutions for industry and healthcare. The aim is about to support people in repetitive, dangerous, and strenuous jobs with technological solutions that integrate with daily life and respect the environment.
We are looking for a talented Robotic Learning Engineer with at least 2 years of experience to join the Carate Brianza headquarters.
Responsibilities
* Design, implement and iterate RL / IL training pipelines for humanoid tasks in simulation and in the real world.
* Design and implement diverse task suites for manipulation, navigation, and whole-body coordination in simulation.
* Manage training experiments and evaluation loops: Hyperparameter tuning, Benchmarking, Logging and Failure analysis.
* Collaborate closely with multidisciplinary teams including: Motion and control engineers, Mechanical design team, Simulation engineers.
* Support sim-to-real transfer by adapting policies to real robot constraints and validating performance during deployment.
Requirements
* >2 years experience in ML / RL / robotics.
* Ms or Phd in Robotics, Automation, Computer Science or related field.
* Strong Python + PyTorch. You can profile, debug numerics, and write maintainable code.
* Familiarity with RL algorithms (PPO, SAC, etc.) and robotics (states, control, kinematics).
* Experience solving real problems using reinforcement learning policies in any domain.
* Strong ownership mindset with ability to document experiments and communicate trade-offs clearly.
Strong Plus
● Experience with:
Isaac Lab / RL Games / Stable Baselines3
● Exposure to:
Imitation Learning / Behavior Cloning
Robotics datasets (state + vision)
● Understanding of:
Sim-to-real challenges
Budget between 35.000€-45.000€, plus welfare 250€
Hybrid approach (1 day working from home per week)