Robot Learning Engineer

About the Role

This is an entry-level research engineering role at an early-stage industrial robotics startup, where you'll apply state-of-the-art machine learning directly to a real robotic work cell on the factory floor. You'll sit at the intersection of ML research and physical deployment, helping automate some of the most demanding manual tasks in industry — including surface finishing, welding, and coating. It's an excellent opportunity for a hungry new grad who wants to ship AI on real hardware from day one.

What You'll Do

• Research and evaluate ML models for robot perception and task understanding.

• Apply computer vision and deep learning to multi-modal sensor data including cameras, force/torque sensors, and depth inputs.

• Design and run experiments with reinforcement learning and imitation learning for robot control.

• Integrate trained AI models into a ROS 2–based robotics stack.

• Iterate rapidly on experiments, maintaining rigorous evaluation standards.

• Bridge the gap between research and real-world factory deployment.

What We're Looking For

• 0–3 years of experience; strong MSc or BSc in Robotics, Computer Science, AI, or a closely related field.

• Solid programming skills in Python, with hands-on experience in PyTorch or TensorFlow.

• Background in computer vision, robot learning, or physical autonomous systems — demonstrated through a thesis, internship, research, or open-source work.

• Familiarity with ROS 2 or a strong motivation to learn it quickly.

• Experience with sim-to-real transfer or simulators such as Isaac Sim, PyBullet, or MuJoCo is a plus.

• Exposure to 3D perception, point clouds, or depth estimation is a plus.

• Research publications or reproducible project work are a bonus signal.

• Must be eligible to work in Germany and able to work on-site in Munich — no visa sponsorship is available.

Compensation & Benefits

Equity participation is included. Cash compensation details will be confirmed during the interview process. No visa sponsorship is available.

Location

On-site in Munich, Bavaria, Germany. This role is not available remotely.