Join a team building intelligent robotic systems for demanding industrial applications. In this entry-level role, you will apply machine learning to physical robots, helping translate research in perception and robot learning into practical factory deployment.
Research and evaluate machine learning models for robot perception and task understanding.
Apply computer vision and deep learning to camera, force and torque, and depth sensor data.
Experiment with reinforcement learning and imitation learning for robot control.
Integrate AI models into a ROS 2 robotics stack.
Run rigorous experiments and iterate on approaches using physical robotic systems.
Connect research results to real-world industrial deployment.
Approximately 0 to 3 years of relevant experience, including research, internships, or practical projects.
A BSc or MSc in robotics, computer science, AI/ML, or a related field, or equivalent practical experience.
Proficiency in Python and experience with PyTorch or TensorFlow.
Computer vision or robot-learning experience involving physical robotic systems.
Fluent English; German is a plus.
Familiarity with ROS or ROS 2, physics simulation tools such as Isaac Sim, PyBullet, or MuJoCo, and sim-to-real transfer is beneficial.
Experience with 3D perception, point clouds, or depth estimation is beneficial, as are robotics or machine learning publications and open-source contributions.
Equity participation is available. Visa sponsorship is not available.
On-site, five days per week in Munich, Bavaria, Germany. Candidates must be eligible to work in Germany.