Lead the design, training, and deployment of reinforcement learning policies for robot motion
Provide senior technical guidance on RL and learning-based control across the team
Own and evolve the RL training infrastructure and sim-to-real pipeline
Shape the technical vision for internal ML tooling and experiment management
Collaborate closely with cross-functional stakeholders to identify how to expand the robot's autonomous operational envelope
Triage field issues related to locomotion
Write, deploy, and maintain efficient Python and C++ software for the learning and locomotion stack

PhD in robotics, machine learning, computer science or a related field with a strong focus on reinforcement learning; alternatively, an equivalent track record of RL research and deployment in robotics
Master's degree from a top-tier technical university (e.g. ETH Zurich, EPFL) in robotics, machine learning, computer science or related field and 5+ years of professional experience
Proven track record of shipping ML models to the field and maintaining those solutions over time
Solid grounding in robot control fundamentals and autonomous systems, including motion control, state estimation, path planning and actuation
Experience using robotic simulation tools such as Gazebo or Isaac Sim
Strong understanding of sim-to-real transfer, domain randomisation, reward shaping, and policy robustness techniques
Proficiency in Python and modern ML frameworks (PyTorch); working knowledge of C++
Strong knowledge of Linux systems and middleware frameworks for integrating learned components into a larger software stack
Pragmatic and solution-oriented mindset
Excellent communication skills in English

Senior Reinforcement Learning Engineer

Key skills