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. 

 

About the Job

We are looking for passionate, skilled interns to join our team—and to actively contribute to the development and deployment of extraordinary construction robots. The ideal candidate should be self-motivated, capable of working autonomously and in small teams, and should have a strong desire to solve exciting, challenging, and applied problems.

At Gravis, we engineer solutions at the nexus of hardware and software every day. Your role would likely explore one or more of the following areas (based on experience and interest).

This is an opportunity to join a dynamic and versatile team, and to be part of a young startup that iswill revolutionizinge heavy construction. You will get the chance to apply your skills to critical tasks, while learning from a team of world-class robotic engineers. Gravis Robotics offers a fair market salary and an excellent working location in the city of Zurich. 

 

We encourage applicants to include a selection of projects they have worked on, focusing on highlighting the programming languages used and individual contributions. If possible, please provide links to relevant repositories (e.g., GitHub), and any additional supporting material.