(Senior) Machine Learning Engineer – Video AI

Company Description

Founded in 2020 and headquartered in Munich, Snke is transforming healthtech with scalable, data-driven innovation powered by AI and big data analytics. We're experts specializing in large platforms, digital health and software-driven medical technology. By delivering a trusted orchestration layer, Snke empowers healthcare providers, societies, registries, agencies and all partners to deploy cutting‑edge solutions for safe and efficient interventions and enhances patient outcomes. Beyond our Munich headquarters, we have core teams in Chicago, Heidelberg, San Diego and Tel Aviv. Snke fosters global collaboration to create technologies that are smart, enabling and holistic—helping healthcare providers deliver meaningful change.

Job Description

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 AI based 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

Qualifications

• Degree in Computer Science, natural sciences, or similar background
• 3+ years of professional experience in using modern machine learning methods along with classic computer vision approaches to solve challenging problems in the area of image processing
• Experience in state‑of‑the‑art ML tooling related to experiment management, containerization, orchestration, processing pipelines and data version control.
• Profound demonstrated experience in developing complex software systems in Python and/or other programming languages
• Ideally, you gained this experience during a range of projects in an industrial setting, or you have worked on a PhD in a relevant area
• Good knowledge in the setup and operation of cloud‑based computing environments (ideally AWS) is a plus
• Experience of working on AI‑based products in the med tech industry is a plus

Additional Information

• A mutually‑supportive, international team
• Opportunity to build career experience in an exciting international company with a lasting impact on medical technology based in Munich
• Flexible working hours
• Secure bicycle storage room
• Subsidized catering service
• Subsidized Gold Gym membership
• Centrally located, modern work spaces with a great212m² roof terrace

Ready to apply? We look forward to receiving your online application including your first available start date.

Contact person: Elisabeth Karro

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