3 months ago
Zürich, SwitzerlandSenior
Responsibilities
- Lead the design, training, and deployment of reinforcement learning policies for robot motion and bridge simulation-to-real-world performance.
- Provide senior technical guidance on reinforcement learning and learning-based control, mentor engineers, and establish policy-development best practices.
- Own and evolve RL training infrastructure and the sim-to-real pipeline for reproducibility, scalability, and rapid iteration.
- Shape internal ML tooling and experiment-management systems, including training dashboards and automated evaluation pipelines.
- Collaborate with stakeholders to expand the robot’s autonomous operational envelope.
- Triage field locomotion issues, identify failure patterns, and improve policy robustness using deployment data.
- Write, deploy, and maintain Python and C++ software for the learning and locomotion stack.
Requirements
- PhD in robotics, machine learning, computer science, or a related field focused on reinforcement learning, or an equivalent track record of RL research and robotics deployment.
- Alternatively, a master’s degree from a top-tier technical university in robotics, machine learning, computer science, or a related field plus 5+ years of professional experience.
- Proven experience shipping ML models to the field and maintaining them over time.
- Strong knowledge of robot control and autonomous systems, including motion control, state estimation, path planning, and actuation.
- Experience with robotic simulation tools such as Gazebo or Isaac Sim.
- Strong understanding of sim-to-real transfer, domain randomisation, reward shaping, and policy robustness.
- Proficiency in Python and modern ML frameworks, particularly PyTorch, plus working knowledge of C++.
- Strong knowledge of Linux systems and middleware frameworks for integrating learned components into larger software stacks.
- Pragmatic, solution-oriented approach balancing research exploration with production delivery.
- Excellent English communication skills.
- Preferred experience includes training and deploying RL policies on physical robots, scalable modular motion-control architectures, navigation and autonomous mobile robots, agentic engineering toolchains, software architecture leadership, and physical-system fundamentals such as multibody dynamics, electromechanical drive physics, energy optimisation, and contact physics.
Benefits
- Fair market salary and an attractive employee stock ownership plan.
- Exciting and dynamic work environment with a fast-growing company and ambitious team.
- Opportunity to leverage experience, contribute ideas, and work on cutting-edge mobile robotics.
- Position based in Zurich.
Categories
ML EngineeringRobotics
