• 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

Core qualifications

• 2–5 years industry experience developing Reinforcement learning systems for control and/or planning and deploying them on real robots with a customer. If you only have experience with simulation, you’re most likely not a good fit for this position.

• 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. 

Great-to-Have Skills & Experience

• Experience with hydraulic machinery

• Experience with supervised learning or imitation learning

• Research experience in reinforcement learning

• Experience deploying robotic systems at scale (e.g. hundreds of units)

• Familiarity with ROS or similar robotics frameworks

• Experience with feature-flagged deployments, staged rollouts, or long-lived platforms

• Experience with data curation for ML applications

• Experience guiding, mentoring, or leading junior colleagues, students, or project teams. 

• Familiarity with or interest in utilizing AI coding tools. 

This Role is a Great Fit If

• You are passionate about building systems that work reliably in the real world

• You want to help build a long-lived excavation planning and control system intended to scale and positively impact the entire construction industry. 

• You are comfortable working with the realities of imperfect data and noisy measurements.

• You have a keen interest in bridging the sim2real gap and understanding the differences between simulation and physical environments. 

• You are excited to help drive technical direction in a growing team transitioning from prototyping to the product stage.

• You value a collaborative team culture rooted in thoughtful design, creative thinking, mutual respect, and pragmatism.