Gravis Robotics is a startup that turns heavy construction machines into intelligent and autonomous robots. 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. Our team has over a decade of academic experience honing the cutting edge of large-scale robotics, and is rapidly growing to bring that expertise into a trillion dollar industry through active deployments with market leaders.
Construction represents 13% of the global GDP, but is stagnating in labor productivity. Unprepared to meet global housing shortages, and to construct the required renewable infrastructure to slow the advance of climate change, the industry is in need of substantial innovation. Gravis Robotics is on a mission to provide such innovation through the automation of heavy construction equipment. Our solution allows a single equipment operator to orchestrate multiple machines at a safe distance. To do so, we developed a specialized heavy-machinery retrofitting kit featuring a number of different sensors, communication and networking devices and compute modules, that runs our learning-based automation software and that enables autonomous operations of heavy construction machines. Gravis' autonomy kit can be retrofitted on any existing heavy machinery, agnostic to the machine's manufacturer, type and age, unlocking the opportunity of deploying our solution anywhere in the world.
We are looking for a passionate, skilled intern 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.
- Procedural scene generation in simulation (terrain, lighting, asset placement)
- Synthetic data generation for training ML models across varied environmental conditions
- Enhancement of physics-based sensor models for LiDAR and cameras
- Automated test creation in simulation for construction site scenarios and machine behaviors (e.g. excavation with truck loading)
- Master's level study (e.g. in robotics, computer science, mechanical or electrical engineering)
- Advanced proficiency in Python
- Working knowledge of C++
- Experience with ML pipelines and 3D perception (e.g. object detection, point cloud processing)
- Experience with robotics simulation tools (e.g. Gazebo, MuJoCo, NVIDIA Isaac Sim)
- Experience with ROS 2 or other robotics frameworks
- Proficiency in Linux and Git
- Familiarity with sensor modeling (LiDAR, cameras) or synthetic data generation
- Experience conducting research (e.g. through an academic lab or previous internships)
- Interest or prior experience in heavy construction or autonomous vehicles
- Experience with software testing (unit, component, or functional tests)
This is an opportunity to join a dynamic and versatile team, and to be part of a young startup that will revolutionize 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.
Gravis is an equal opportunity employer. We are committed to inclusion and diversity, and do not discriminate based upon race, color, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics. All qualified people are highly encouraged to apply.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
