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# Entry-Level Robot Learning Engineer

- **Company:** Clera
- **Location:** München, Germany
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Python (Programming Language), Machine Learning, Tensorflow, Robotic Automation Software, Reinforcement Learning, Pytorch, Transfer Learning, Deep Learning, Isaac Sim, Information Technology, Free and Open-Source Software, Lidar
- **Published:** October 11, 2026
- **Apply:** https://www.careerjet.de/jobad/de9e7671770aaf64c5affbd281b6982fa2

## About the Role

Approximately 0 to 3 years of relevant experience, with a BSc or MSc in robotics, computer science, AI, machine learning, or a related field, or equivalent practical experience.
Proficiency in Python and experience with PyTorch or TensorFlow.
Exposure to computer vision or robot learning, with an interest in applying ML to physical systems.
Fluency in English; German is a plus.
Familiarity with ROS or ROS 2 is helpful.
Experience with simulation or sim-to-real transfer, such as Isaac Sim, PyBullet, or MuJoCo, and familiarity with 3D perception, point clouds, or depth estimation are beneficial.
Robotics or machine learning research publications or open-source contributions are also beneficial.
Compensation & Benefits
Equity participation is available. Visa sponsorship is not available, so candidates must be eligible to work in Germany.

## Description

Join a robotics and machine learning team developing intelligent systems for demanding industrial tasks. You will apply current machine learning methods to real robotic hardware, helping bridge research and practical 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 rigorous experiments, iterate on results, and support deployment on real robotic systems.