Senior Reinforcement Learning Engineer

3 мес. назад

SwitzerlandEuropeSeniorHybrid

reinforcement learningpolicy optimisationroboticssimulationsoftware developmentcode design

Senior Reinforcement Learning Engineer role focused on developing and deploying RL policies for autonomous robots in industrial environments.

is a fast-growing tech company dedicated to shaping the future of mobile robotics across multiple industries. Join our highly talented and motivated team of more than 200 people and work on cutting-edge robot technology. The Opportunity  We are looking for a Senior Reinforcement Learning Engineer to join our Motion Intelligence team. You should bring deep expertise in reinforcement learning and policy optimisation, grounded in real-world robotics experience, and be excited to push the boundaries of autonomous robots. We are looking for someone who can provide senior-level RL and learning-based control guidance across the team — shaping how we train, deploy, and maintain learned policies on physical robots operating in demanding industrial environments. Beyond RL expertise, w

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

Experience training and deploying RL policies on physical robots — not just in simulation
Development of scalable and modular robot architectures of motion control systems
Experience with navigation systems and autonomous mobile robot operation in unstructured environments
Interest in agentic engineering toolchains
Experience leading software architecture design and software engineering best practices
A solid grasp of physical systems, specifically multibody dynamics, electromechanical drive physics, energy optimization, and contact physics, to ensure learned policies respect hardware limits and transfer successfully from simulation to the real world

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