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.
The Gravis RACK is a machine-agnostic retrofit kit that adds autonomy to excavators and wheel loaders from 10 to 100+ tonnes: LiDAR and camera sensing, GNSS RTK, networking hardware and rugged edge compute that works offline.
Paired with the Slate tablet and our Copilot software, it lets an operator run a machine manually, with AI assistance, or fully autonomously.
Increasingly, we also build custom hardware to adapt our machines for highly specialized, robust applications beyond traditional excavation.
Whether these controllers work on the machine depends on how well we close the sim2real gap.
In this role you will help us bridge the gap.
We are looking for someone with strong ML/RL background and experience with real robotic systems.
What you will do Machine & dynamics modeling Build ML models to help bridge the sim2real gap Decide what architecture the problem actually needs - sequence models, state-space formulations, something else - and back the choice with data Characterize where the gap actually comes from: which unmodeled effects hamper the sim2real transfer, and which we can safely ignore Answer how much data is needed and what distribution it has to cover Performance monitoring Define the performance metrics and validation methodology for model fide.