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# Data Operations & Labeling Specialist

- **Company:** STARK GmbH
- **Location:** München, Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Python (Programming Language), Machine Learning, SQL Databases, Lidar
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/data-operations-labeling-specialist-all-genders-stark-9006397

## About the Role

You are the crucial bridge between our raw field data, our external labeling partners, and our internal Machine Learning teams. Your mission is to ensure our AI models are trained on the highest quality data possible. You will own the day-to-day operations of the data labeling lifecycle: curating raw data, preparing annotation batches, managing vendor communication, and rigorously assessing the quality of incoming labels. If you are highly organized, detail-oriented, and interested in the intersection of data operations and Computer Vision, this is the perfect place to start your career in AI., * Highly organized and detail-oriented: you can manage multiple data batches, vendor deliveries, and QA processes simultaneously without dropping the ball.

 * Strong communication skills: you are comfortable coordinating with external vendors and writing clear, unambiguous instructions/guidelines.
 * Basic understanding of Computer Vision and Machine Learning concepts (e.g., bounding boxes, segmentation masks, object tracking).
 * Comfortable with basic scripting (Python) and data querying (SQL) to automate small tasks or filter data batches.
 * Pragmatic problem-solver who enjoys bringing order to chaotic data deliveries.
 * Fluent in English., * Familiarity with annotation formats (like COCO) and ML dataset structures.
 * Previous experience using data annotation platforms (CVAT, Labelbox, Scale AI, etc.).
 * Exposure to sensor data (RGB, Thermal, LiDAR) or robotics domains.

## Description

* Own the day-to-day data labeling lifecycle: curate raw field data, create annotation batches, and prepare deliveries for external labeling companies.
 * Serve as the primary point of contact for external data annotation vendors, clarifying edge cases and providing feedback on labeling guidelines.
 * Conduct rigorous Quality Assurance (QA) assessments on incoming label deliveries, track error rates, and create performance reports.
 * Support data curation efforts, filtering and selecting the most valuable sensor data (images, video, lidar) for model training.
 * Work closely with ML Engineers to understand their data needs, edge cases, and specific Computer Vision requirements.
 * Help maintain the data catalog by ensuring incoming datasets are properly tagged and logged.


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