Senior Reinforcement Learning Engineer

2 months ago

Zürich, Switzerland or Austin, TX, USAMid Level / Senior

Responsibilities

Develop data-driven planning and control systems for autonomous excavation that generalize across machine models and soil conditions.
Improve simulation to reduce the sim-to-real gap and define data collection and curation pipelines for policy training.
Design experiments to improve system performance and robustness, including adaptive and online reinforcement learning in deployed systems.
Integrate learned components into the broader robotics software stack and collaborate with excavation and motion-planning engineers.
Build tools to analyze and evaluate learned-component behavior.
Mentor and supervise junior team members, interns, and students.

2–5 years of industry experience developing reinforcement-learning systems for control or planning and deploying them on real robots with customers.
Experience with GPU-accelerated simulation environments such as IsaacSim, IsaacLab, CARLA, or MuJoCo.
Strong Python skills and experience with PyTorch or similar libraries.
Proficiency in C++.
Experience working with physical robots, sim-to-real transfer, and production deployment of robotic systems.
Ability to debug real-world system behavior and willingness to travel as required by business projects.
Preferred qualifications include hydraulic machinery, supervised or imitation learning, reinforcement-learning research, large-scale robotic deployments, ROS, feature-flagged or staged deployments, ML data curation, and mentoring or team leadership experience.

Working location in Zurich with the opportunity to discuss a preferred working arrangement during the interview process.
Work-life balance and flexibility are emphasized.
English proficiency is required for all roles.

51-200 employees