Gravis Robotics is a high-growth Series A start-up backed by SoftBank, bringing Physical AI to the construction industry, turning heavy construction machines into autonomous robots.
Gravis began as an ETH Zurich spin-out, and our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment.
Backed by deep robotics research and now deployed across multiple countries with leading construction and equipment partners, our team is rapidly growing to bring this technology to a trillion-dollar industry.
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.
