Senior Reinforcement Learning Engineer

Zurich, Switzerland

Full Time

Posted May 26, 2026

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ANYbotics is a fast-growing tech company dedicated to shaping the future of mobile robotics across multiple industries.

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