Senior Reinforcement Learning Engineer

About the Job

 
The autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. You will build control modules that run on many different machines, across many sites, with different soil conditions. We’re looking for a roboticist with data driven planning and/or control background, deep python expertise and good level of C++ proficiency.
 
To be successful in this role you should have experience working with real robots, tackling the challenges of sim2real transfer, and deploying robotic systems in a production environment.
 
The autonomy team at Gravis builds autonomous systems for excavators operating in real construction environments. In this role, you will develop control modules designed to run across diverse machines, sites, and soil conditions. We are looking for a roboticist with a background in data-driven planning and/or control, strong Python skills, and a solid working knowledge of C++.
 
To thrive in this role, you should have experience working with physical robots, navigating the challenges of sim-to-real (sim2real) transfer, and deploying robotic systems into production environments.

What you will do

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

What we’re looking for

We recognize that excellent candidates come from diverse backgrounds with various combinations of skills. If you meet most of the core qualifications below, we highly encourage you to apply.

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.