Robot Learning Engineer

Job description

### About the Role This is an entry-level research and engineering role at an early-stage industrial robotics startup, where you will apply state-of-the-art machine learning directly to a physical robotic work cell built for demanding manufacturing tasks like surface finishing, welding, and coating. You will sit on a small, ambitious team in Munich and own the full loop from research to real-world factory deployment. ### What You'll Do - Research and evaluate ML models for robot perception and task understanding. - Apply computer vision and deep learning to sensor data including cameras, force/torque sensors, and depth inputs. - Experiment with reinforcement learning and imitation learning for robot control. - Integrate trained AI models into a ROS 2 robotics stack. - Run rigorous experiments, measure results carefully, and iterate quickly. - Bridge the gap between research findings and practical deployment on factory hardware. ### What We're Looking For - BSc or MSc in Robotics, Computer Science, AI/ML, or a closely related field, or equivalent hands-on experience. - 0 to 3 years of experience, including new graduates with strong fundamentals and relevant project or thesis work. - Proficiency in Python and experience with PyTorch or TensorFlow. - Background touching robot learning, computer vision, or ML for physical systems; a pure NLP or LLM focus is not a strong fit. - Genuine enthusiasm for working with physical systems and deploying AI on real robots. - Eligible to work in Germany and able to commit to full-time, on-site work in Munich. - Fluent English; German is a plus. - Familiarity with ROS or ROS 2 is a bonus. - Experience with sim-to-real transfer or physics simulators such as Isaac Sim, PyBullet, or MuJoCo is a bonus. - Exposure to 3D perception, point clouds, or depth estimation is a bonus. - Research publications or open-source contributions in robotics or ML are a bonus. ### Compensation & Benefits Equity participation is included. Visa sponsorship is not available for this role. ### Location On-site, five days per week in Munich, Bavaria, Germany.