ML Ops​/Data Infrastructure Engineer Surgical AI

The University of Zurich, Switzerland's largest university, offers a range of attractive positions in various subject areas and professional fields. With around 10,000 employees and currently 12 professional apprenticeship streams the University offers an inspiring working environment on cutting-edge research and top‑class education. Put your talent and skills to work with us. Find out more about UZH as an employer!

• 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
• Leveraging NVIDIA technology for accelerating deployment of ML models
• Deployment of simulation environments

• 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

• 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

• 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 safe, reliable clinical translation of AI‑enabled systems

• Degree from 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/Git Hub workflows, and containerization
• Motivation to work in a translational, interdisciplinary environment connecting AI, robotics, and clinical research
• English is the main working language;
  German is an added advantage

Joelle Kunz
Assistant

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