Senior Reinforcement Learning Engineer

About the role

The autonomy team at Gravis Robotics builds autonomous systems for excavators operating in real construction environments. You will develop data-driven planning and control systems that generalize across machine models and soil conditions, while addressing the challenges of sim2real transfer. This role involves integrating learned components into a larger software stack and collaborating with excavation and motion planning engineers to deploy robust robotic systems.

Responsibilities

• 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
• Integrate learned components into a larger software stack
• Build tools for analysing and evaluating the behavior of learned components

Required skills

• Reinforcement learning
• Python
• PyTorch
• C++
• Robotics
• Sim2real
• Control systems
• Planning systems

Nice to have

• Hydraulic machinery
• Supervised learning
• Imitation learning
• IsaacSim
• IsaacLab
• CARLA
• MuJoCo
• ROS
• Data curation

Qualifications

• 2–5 years industry experience developing Reinforcement learning systems for control and/or planning and deploying them on real robots

About the Company

Gravis Robotics is a high-growth Series A start-up backed by SoftBank, bringing Physical AI to the construction industry by turning heavy construction machines into autonomous robots. The company began as an ETH Zurich spin-out and is currently deploying technology across multiple countries with leading construction and equipment partners.