About Us

STARK is a new kind of defence technology company revolutionizing the way autonomous systems are deployed across multiple domains. We design, develop and manufacture high-performance unmanned systems that are software-defined, mass-scalable, and cost-effective. This provides our operators with a decisive edge in highly contested environments.

We're focused on delivering deployable, high-performance systems — not future promises. In a time of rising threats, STARK is bolstering the technological edge of NATO Allies and their Partners to deter aggression and defend Europe — today.

About the team

The Data Operations team owns the entire data lifecycle behind STARK's AI stack: collection, acquisition, generation, curation, and management. We run our own data-collection campaigns across Europe, evaluate new sensors and platforms, and build the internal data platform that turns raw recordings into ready-to-use datasets. Everything we produce feeds directly into the perception and autonomy systems deployed on STARK's platforms — a real data advantage is built, not bought. The team is scaling up right now: real scope, direct impact, no legacy.

Your mission

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.

Responsibilities

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Own the day-to-day data labeling lifecycle: curate raw field data, create annotation batches, and prepare deliveries for external labeling companies.

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Serve as the primary point of contact for external data annotation vendors, clarifying edge cases and providing feedback on labeling guidelines.

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Conduct rigorous Quality Assurance (QA) assessments on incoming label deliveries, track error rates, and create performance reports.

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Support data curation efforts, filtering and selecting the most valuable sensor data (images, video, lidar) for model training.

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Work closely with ML Engineers to understand their data needs, edge cases, and specific Computer Vision requirements.

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Help maintain the data catalog by ensuring incoming datasets are properly tagged and logged.

Qualifications

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Highly organized and detail-oriented: you can manage multiple data batches, vendor deliveries, and QA processes simultaneously without dropping the ball.

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Strong communication skills: you are comfortable coordinating with external vendors and writing clear, unambiguous instructions/guidelines.

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Basic understanding of Computer Vision and Machine Learning concepts (e.g., bounding boxes, segmentation masks, object tracking).

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Comfortable with basic scripting (Python) and data querying (SQL) to automate small tasks or filter data batches.

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Pragmatic problem-solver who enjoys bringing order to chaotic data deliveries.

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Fluent in English.

Nice to have

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Familiarity with annotation formats (like COCO) and ML dataset structures.

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Previous experience using data annotation platforms (CVAT, Labelbox, Scale AI, etc.).

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Exposure to sensor data (RGB, Thermal, LiDAR) or robotics domains.

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