RobCo is spearheading a revolution in modular robotics - making automation radically easier, more flexible, and more intelligent. With over €50 million in funding from world-class investors such as Sequoia Capital and Lightspeed, we are building the category-leading robotics company in Europe and the US.

As a Mechatronics Engineer in the Robot Learning team, you will prototype, design and build the physical systems that enable robots to learn from the real world. Your work will span robot-specific hardware, teaching devices, camera mounts, experimental end-effectors, and custom mechatronic setups used for data collection, tele-operation, and learning-based autonomy.

This is a deeply hands-on role. You will rapidly prototype mechanical and electromechanical systems, iterate quickly, and work closely with robot learning, autonomy, and software teams to bring ideas from concept to working hardware — often within days, not months.

If you enjoy working at the intersection of CAD, fabrication, electronics, and code, and want to build physical systems that directly influence robot intelligence, this role is for you.

• Design & prototype mechatronic systems - Build robotic hardware, teaching devices, mounts, fixtures, and experimental mechanisms for robot learning and autonomy.

• Hands-on CAD & fabrication - Create CAD designs and rapidly prototype using 3D printing; design parts for CNC machining and external fabrication when needed.

• Electronics prototyping - Select and integrate sensors, actuators, cameras, and microcontrollers; build and debug simple electronics, wiring, and PCBs.

• Evaluation of existing products: You will work with suppliers to evaluate existing products.

• Tool-based iteration - Operate workshop tools, assemble prototypes, solder, modify, and troubleshoot hardware directly in the lab.

• System integration - Ensure mechanical designs integrate cleanly with robot arms, perception systems, and learning pipelines.

• Support experiments & deployments - Build one-off and short-run hardware used in data collection, demonstrations, and real-world robot experiments.

• Collaborate closely with software & ML teams - Translate algorithmic and experimental needs into physical systems that work reliably.

• Document & improve - Capture designs, learnings, and best practices to enable reuse and faster iteration over time.