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Senior Reinforcement Learning Engineer

GRAVIS ROBOTICS
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Hybrid (Zurich), Hybrid (Austin), Hybrid (Oxford)Full-timePosted 3mo ago

About the role

Autonomy team role focused on learning-based planning and control for autonomous excavation systems, deploying data-driven RL components to real robots across multiple sites and machine types.

Responsibilities

  • Learning-Based Planning and Control for Real Systems - Develop data driven planning and control systems for autonomous excavation that generalize across machine models and soil conditions
  • Contribute to simulation improvements that reduce or address the sim2real gap
  • Define data collection and curation pipelines for incorporating real data in policy training
  • Design experiments focused on continuous performance and robustness improvements
  • Explore the usage of adaptive and online reinforcement learning in deployed systems
  • Provide mentorship and supervision for junior team members, interns, and students
  • System Integration - Integrate learned components into a larger software stack
  • Collaborate with excavation and motion planning engineers
  • Build tools for analysing and evaluating the behavior of learned components

Qualifications

  • 2–5 years industry experience developing Reinforcement Learning systems for control and/or planning and deploying them on real robots with a customer
  • Experience with GPU accelerated simulation environments (e.g. IsaacSim/IsaacLab, CARLA, MuJoCo)
  • Strong Python skills and experience with PyTorch or similar libraries
  • Proficiency in C++
  • Comfortable debugging real-world system behavior
  • Ability and willingness to travel as required by business projects.

Skills

Required:Reinforcement Learning for control and planningPythonC++GPU-accelerated simulation toolsDebugging real-world systems
Nice to have:Mentoring/leadershipROS or robotics frameworks
Last updated Oct 21, 2026. Interested? Apply on the company site.

About GRAVIS ROBOTICS

Heavy machinery, automated.

With over a decade of experience in construction robotics, our team has developed the world's most advanced robotic excavators.

automationteleoperationexcavatorsrobots
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