ML Ops / Data Infrastructure Engineer / Engineeress for Surgical AI

Universität Zürich - January 7, 2026

Join the University of Zurich

The University of Zurich, Switzerland's largest university, offers a variety of attractive positions across different subject areas and professional fields. With approximately 10,000 employees and 12 professional apprenticeship streams, the University provides an inspiring working environment centered around cutting-edge research and top-class education. Put your talent and skills to work with us and discover more about UZH as an employer!

Your Responsibilities

MLOps & Model Integration

  • Deploy, monitor, and maintain machine learning models for surgical applications on HPC and edge devices within OR-X and ROSI research infrastructure.
  • Develop CI/CD pipelines for model lifecycle management, automated testing, and continuous deployment.
  • Leverage NVIDIA technology to accelerate the deployment of ML models.
  • Deploy simulation environments.

Data Engineering & Infrastructure

  • Integrate multimodal data streams (video, kinematics, tracking, imaging, sensor data) into the central AI infrastructure.
  • Develop APIs, data ingestion pipelines, and real-time streaming frameworks.
  • Structure and pre-process multimodal surgical datasets for model training and downstream analytics.
  • Develop a distribution strategy that enables external researchers to access the data.

AI Deployment in Surgical Workflows

  • Work closely with AI researchers to operationalize models for surgical scene understanding, workflow prediction, skill assessment, and mixed reality.
  • Develop monitoring tools to ensure robustness, reliability, and latency compliance for real-time surgical applications.
  • Collaborate with robotics engineers to interface AI pipelines with devices accessible through ROS2 for control and visualization.

System Testing & Validation

  • Support verification and validation experiments in realistic ex-vivo settings.
  • Implement performance monitoring, logging dashboards, and evaluation frameworks for deployed AI models.
  • Contribute to guidelines and best practices for the safe and reliable clinical translation of AI-enabled systems.

Your Profile

  • Degree from a University of Applied Sciences or higher in Computer Science, Electrical Engineering, Robotics, or a related field.
  • Strong experience in MLOps, including Docker, Kubernetes, CI/CD pipelines, model serving, and workflow orchestration tools.
  • Strong programming skills in C++, Python, and related languages.
  • Experience with data engineering, data pipelines, and multimodal dataset handling.
  • Proficiency in interfacing with AI infrastructures, preferably with experience in NVIDIA AI technologies. Experience with Holoscan is an asset.
  • Familiarity with Nvidia hardware (DGX, Spark, Jetson).
  • Experience with ROS2 and real-time systems.
  • Comfortable in Linux/Ubuntu environments, Git/GitHub workflows, and containerization.
  • Motivated to work in a translational, interdisciplinary environment connecting AI, robotics, and clinical research.
  • English is the main working language; proficiency in German is an added advantage.

Apply online using the form below. Only applications matching the job profile will be considered.

Contact Information

Joelle Kunz
Assistant
+41 44 510 73 66
Joinrocs@balgrist.ch

Location : Zürich
Country : Switzerland

Application Form

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