Overview
ABOUT THE ROLE
Full job description
ABOUT THE ROLE Join a small industrial robotics team applying machine learning to real-world robotic work cells. In this entry-level research role, you will develop and evaluate approaches in perception, reinforcement learning, imitation learning, and sim-to-real transfer, helping bring intelligent automation to challenging industrial tasks. WHAT YOU'LL DO
- Research and evaluate machine learning models for robot perception and task understanding.
- Apply computer vision and deep learning to camera, depth, and force or torque sensor data.
- Experiment with reinforcement learning and imitation learning approaches for robot control.
- Integrate AI models into a ROS 2 robotics stack.
- Run rigorous experiments, analyze results, and iterate on models and system performance.
- Help bridge research prototypes and deployment on real factory hardware. WHAT WE'RE LOOKING FOR
- Approximately 0 to 3 years of relevant experience, with a background in robot learning, robotics, computer vision, or machine learning for physical systems.
- A bachelor's or master's degree in robotics, computer science, AI, machine learning, or a related field, or equivalent practical experience.
- Proficiency in Python and experience with a deep-learning framework such as PyTorch or TensorFlow.
- Exposure to computer vision or robot learning, and familiarity with ROS or ROS 2.
- Fluent English; German is a plus. Familiarity with simulation tools, sim-to-real transfer, 3D perception, point clouds, or depth estimation is helpful. Research publications or open-source contributions are also valued. COMPENSATION & BENEFITS Equity participation is available. Visa sponsorship is not available, and candidates must be eligible to work in Germany. LOCATION On-site, five days per week in Munich, Bavaria, Germany.
Tips for this job
Practical JobOpportunity guidance. These tips do not replace official rules or create new eligibility requirements.
- Tailor the CV and application to the responsibilities and required skills stated on the official employer page.
- Use concrete evidence of relevant work, projects and measurable results rather than generic claims.
- Confirm location, work authorization, remote restrictions and sponsorship terms before applying.
- Apply through the original employer or official recruitment destination shown on this page.
Verification notes
laptop-ats-crawler v3
JobOpportunity is the discovery and verification layer. Confirm eligibility, dates, salary/funding and application instructions on the original source before submitting anything.
Apply through JobOpportunity →