Working Student - Labelling (f/m/d)

At TYTAN, we set a global standard in autonomous air defense with AI-drive, cost-efficient systems that counter unmanned aerial threats at scale. We protect people. We protect critical infrastructure. We protect European sovereignty.

We are now looking for a team member who wants to make an impact. As a Working Student in Data Labeling (m/f/d), you will support the team by preparing and managing perception data, ensuring dataset quality, help organize and collect flight recordings used to train our AI models.

Responsibilities

• Annotate and curate perception datasets using our internal annotation tools and help improve it
• Maintain and improve the quality of existing labeled datasets
• Organize and manage large-scale perception datasets and recording structures (UAV with cameras)
• Coordinate with the flight testing team to plan and collect new training data
• Assist with organizing recorded flight data and metadata

Your Profile

• Currently enrolled in Computer Science, Robotics, Electrical Engineering, or a related technical field
• Strong attention to detail and ability to produce high-quality, consistent annotations
• Comfortable working with Linux-based systems
• Basic experience with Python scripting
• Interest in computer vision, machine learning, or robotics
• Ability to work independently and take ownership of processes
• Good communication skills and ability to coordinate across teams

Nice to Have

• Experience with computer vision datasets (e.g. COCO, KITTI, YOLO formats)
• Experience using annotation tools (CVAT, Label Studio, Labelme, etc.)
• Basic understanding of object detection, tracking, or segmentation models
• Experience with Git / DVC

About us

At TYTAN, it is more than just a job; it is about embracing autonomy, personal growth, and purpose. Make a lasting impact in the new era of defense! Join us to create a safer world for all.

TYTAN is an equal-opportunity employer committed to fairness and inclusivity. We do not prioritize any specific religion, gender, nationality, or background. Due to security clearance requirements, affiliations with countries listed under § 13 para. 1 no. 17 SÜG may affect the application process. All qualified applicants are encouraged to apply.