Senior Reinforcement Learning Engineer

pANYbotics is a fast-growing tech company dedicated to shaping the future of mobile robotics across multiple industries. /ph3Key Responsibilities /h3ulliLead the design, training, and deployment of reinforcement learning policies for robot motion — bridging the gap from simulation to reliable real-world performance /liliProvide senior technical guidance on RL and learning-based control across the team, mentoring engineers and establishing best practices for policy development workflows /liliOwn and evolve the RL training infrastructure and sim-to-real pipeline, ensuring reproducibility, scalability, and fast iteration cycles /liliShape 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 /liliCollaborate closely with cross-functional stakeholders to identify how to expand the robot's autonomous operational envelope /liliTriage field issues related to locomotion, recognise failure patterns, and rapidly improve policy robustness based on real deployment data /liliWrite, deploy, and maintain efficient Python and C++ software for the learning and locomotion stack /li /ulh3Requirements /h3ulliPhD 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 /liliMaster'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 /liliProven track record of shipping ML models to the field and maintaining those solutions over time /liliSolid grounding in robot control fundamentals and autonomous systems, including: motion control, state estimation, path planning and actuation /liliExperience using robotic simulation tools such as Gazebo or Isaac Sim /liliStrong understanding of sim-to-real transfer, domain randomisation, reward shaping, and policy robustness techniques /liliProficiency in Python and modern ML frameworks (PyTorch); working knowledge of C++ /liliStrong knowledge of Linux systems and middleware frameworks for integrating learned components into a larger software stack /liliPragmatic and solution-oriented mindset — comfortable balancing research exploration with production delivery /liliExcellent communication skills in English /li /ul #J-18808-Ljbffr