Working Student (m/f/d) Data Science Computer Vision / IdentityCheck

CHECK24 · München, Bayern, Germany

ExclusivePart-timePublished Jun 22, 2026

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About the role

  • You prepare training datasets through precise labeling for machine learning training runs
  • Using image editing software, GenAI, and computer vision technologies, you create datasets for different application scenarios
  • Machine learning methods and classical image processing techniques are applied and tested in product-oriented use cases
  • You conduct experiments and evaluate results to optimize existing models and processes
  • In the development of data-driven solutions, you support the Data Science team in daily operations
  • You are currently enrolled in a degree program in Mathematics, Computer Science or a comparable field
  • Solid Python skills support your daily work
  • You have already gained initial practical experience with machine learning methods in the field of computer vision
  • A precise and detail-oriented approach to datasets and experiments defines your way of working
  • You are interested in computer vision and machine learning applications on mobile devices such as Android and iOS
  • Experience with Photoshop, GIMP, or similar image editing software is beneficial
  • Good English skills (minimum C1 level), German skills are a plus
  • A strong start to your professional career – with structured onboarding and a buddy system to support your integration into the team
  • Flexible working hours – adapt your working schedule to fit around your studies
  • Shared lunches – enjoy free lunch four times a week together with your colleagues
  • Health & fitness – benefit from subsidized external sports programs (including EGYM Wellpass) as well as free daily sports courses at the office
  • Events & team spirit – as a Working Student (m/f/d), you will naturally take part in all company events and celebrate successes together with the team
  • Excellent accessibility – centrally located office at Donnersbergerbrücke with outstanding public transport connections

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