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# Working Student Robot Teaching & VLA Data Engineering

- **Company:** Agile Robots Ag
- **Location:** München, Germany
- **Contract:** Permanent contract
- **Skills:** Training Data, Agile Methodology, Artificial Intelligence, Information Engineering, Python (Programming Language), Machine Learning, Scientific Computating, Pytorch, Information Technology
- **Published:** July 16, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=f2fdc80abc764e4f

## About the Role

* Academic Background: Currently enrolled in a Master's programme in Robotics, Machine Learning, Computer Science, Mechanical Engineering, or a closely related technical field.
 * Python: Solid Python skills and working familiarity with common ML/scientific computing libraries.
 * ML Basics: Working understanding of machine learning fundamentals and hands-on experience with PyTorch., * Robotics Context: Prior exposure to robot learning workflows, teleoperation systems, or demonstration data collection.
 * Learning Methods: Familiarity with imitation learning, behavioral cloning, or VLA/world-model approaches applied to robotic or embodied tasks.
 * Interdisciplinary Experience: Demonstrated work or study experience spanning hardware, software, and AI.

## Description

The Industrial Automation & Robotic Applications Department of Agile Robots is looking for a Working Student (m/f/d) Robot Teaching & VLA Data Engineering, who will teleoperate an industrial semi-humanoid robot and evaluate the resulting training data as it is taught, at a customer production site, to pick up workpieces and place them into a fixture. The robot learns this task through Vision-Language-Action (VLA) models, which are trained on the demonstrations this role records., * Teleoperation & Demo Recording: Set up and operate the robot to record training demonstrations directly at the hardware, using teleoperation devices such as Universal Manipulation Interfaces (UMIs), GELLO devices, space mice, and classical programming approaches.

 * Data Quality Review: Check and evaluate recorded demonstrations for quality and usability, flagging issues that could affect downstream training.
 * Method Improvement: Work with the AI and Software teams to identify how data collection, teleoperation setup, and training methods can be improved.
 * Safe Operation: Follow site safety protocols when operating the robot in a live customer production environment.
 * Documentation: Keep clear records of recording sessions, data quality findings, and process changes so results are traceable and reproducible.


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