Snke

(Senior) Machine Learning Engineer – Video AI

Snke München

Stellenbeschreibung:

The big amount of data generated in today’s high-tech operating rooms offers a wide range of opportunities for surgical process improvements: Besides optimization of documentation tasks and support of clinical education, this data serves as basis for any intelligent task automatization and smart assistance tools that aim for improving surgical procedures in terms of safety, quality and cost.

In this position, you will be part of the emerging topic of Surgical Workflow Analysis by combining video with other sensor data in the OR. Together with a team of experienced and enthusiastic software developers and machine learning engineers, you will create applications and services - running on the Snke platform - that extract data points to improve surgeries. You need to share our passion for new software technologies and our curiosity to step into the unknown.

We are looking for a Machine Learning Engineer with experience in state-of-the-art ML training platforms and a strong software engineering background.

In this position, you will

  • Work on learning-based solutions for a variety of tasks in medical data analysis with a particular focus on the processing of surgical videos (e.g. event detection, image segmentation, object detection and more)
  • Participate in all phases of the machine learning development life cycle (from requirements engineering and data processing to experimentation, model development/training, and ultimately deployment of solutions)
  • Push the limits of intelligent software components for surgical procedure analysis, making use of the ever increasing amounts of video data
  • Shape the development and productization of AIbased video solutions for medical use-cases
  • Contribute to our success with your creative ideas and your independent and self-responsible way of working, ultimately impact the daily work of medical professionals around the world

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Stelleninformationen

  • Veröffentlichungsdatum:

    02 Jan 2026
  • Standort:

    München
  • Typ:

    Vollzeit
  • Arbeitsmodell:

    Vor Ort
  • Kategorie:

  • Erfahrung:

    2+ years
  • Arbeitsverhältnis:

    Angestellt

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