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Entry-Level Robot Learning Engineer

On-site
CleraMunich, DE23 hours agoWebsite
Fresh
Full-time
Entry / Junior
Engineering

Compensation

Salary undisclosed
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Description

About the Role

Join an industrial robotics team applying machine learning to challenging manufacturing tasks. You will develop and evaluate robot learning approaches, then help bring them from research into real-world factory deployment.

What You'll Do

  • 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 experiments, analyze results, and iterate on approaches.

  • Help bridge robotics research with deployment on industrial hardware.

What We're Looking For

  • Approximately 0 to 3 years of experience, with a bachelor's or master's degree in robotics, computer science, AI/ML, 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.

  • Familiarity with ROS or ROS 2 is helpful.

  • Experience with simulation or sim-to-real transfer, such as Isaac Sim, PyBullet, or MuJoCo, is helpful.

  • Familiarity with 3D perception, point clouds, or depth estimation is helpful. Research publications or open-source contributions in robotics or machine learning are also valued.

  • Fluent English is required; German is a plus.

  • Must be eligible to work in Germany and able to work on-site in Munich five days per week.

Compensation & Benefits

Equity participation is available. Visa sponsorship is not available.

Location

On-site in Munich, Bavaria, Germany, five days per week.

Stack

Computer VisionPythonPyTorchMachine LearningTensorFlowDeep LearningReinforcement LearningRobotics
Posted
Oct 17, 2026
Last seen
Oct 17, 2026
First seen
Oct 17, 2026

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