ML Ops / Data Infrastructure Engineer for Surgical AI

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

• Leveraging NVIDIA technology for accelerating deployment of ML models

• Deployment of 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 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/GitHub 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

The Operating Room-X (OR-X) is a national unique research infrastructure and surgical translation center designed to advance surgical innovation. It combines a fully equipped, realistic operating room with an advanced digital ecosystem that supports ex-vivo surgical experiments, multimodal data acquisition, robotics, AR/VR systems, and high-performance AI computing. A core element of OR-X is its newly established data infrastructure, which enables the synchronized collection, structuring, and streaming of multimodal surgical data through custom hardware interfaces, integrated middleware, and a high-performance computing (HPC) backbone. This infrastructure is already operational and forms the foundation for scalable development and deployment of surgical AI applications. In parallel, the hospital together with the OR-X is building a new platform for robotic surgery and intelligent assistance, bringing together robotics, simulation, AI, and data science. Within this ecosystem, we are seeking a ML Ops / Data Infrastructure Engineer for shaping the underlying data, hardware, and computing infrastructure that enables machine learning, robotics, and real-time surgical AI across OR-X. The role focuses on bridging multimodal data pipelines, HPC systems, and real surgical workflows to enable reliable, real-time AI functionality in translational and experimental settings., * Active participation in a rapidly growing and internationally recognized Surgical Data Science ecosystem * The opportunity to shape the next generation of AI-driven surgical technologies, integrating AR, robotics, and intelligent assistance systems * A highly innovative environment at the intersection of engineering, AI research, and clinical practice at the University Hospital Balgrist * Collaboration with leading academic and industrial partners (ETH AI Center, NVIDIA, Microsoft, ZHAW, and others) * A supportive, motivated, and interdisciplinary team that values creativity, collaboration, and impact