Senior Reinforcement Learning Engineer

• Lead the design, training, and deployment of reinforcement learning policies for robot motion — bridging the gap from simulation to reliable real-world performance • Provide senior technical guidance on RL and learning-based control across the team, mentoring engineers and establishing best practices for policy development workflows • Own and evolve the RL training infrastructure and sim-to-real pipeline, ensuring reproducibility, scalability, and fast iteration cycles • Shape the technical vision for internal ML tooling and experiment management (e.g. training dashboards, automated evaluation pipelines), driving efficiency and rigour across the team's learning workflows • Collaborate closely with cross-functional stakeholders to identify how to expand the robot's autonomous operational envelope • Triage field issues related to locomotion, recognise failure patterns, and rapidly improve policy robustness based on real deployment data • 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 Or • 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 — comfortable balancing research exploration with production delivery • Excellent communication skills in English