Research Engineer in Medical Image Processing and Machine Learning

Main responsibilities

The Research Engineer will architect, expand, and maintain a high-performance, containerized computing platform dedicated to medical data analysis and artificial intelligence workflows. In this role, the engineer will design and deploy scalable MLOps pipelines for automated model training, experiment tracking, and clinical inference, while managing Kubernetes cluster orchestration, distributed storage systems, and multi-tenant GPU resource allocation. Key technical responsibilities include integrating healthcare data infrastructure such as DICOM servers, PACS interfaces, and web-based visualization tools as well as developing active learning pipelines and interactive annotation interfaces for human-in-the-loop validation. The engineer will also build secure RESTful APIs and backend microservices, implement identity and access management protocols compliant with healthcare data privacy standards, and maintain automated CI/CD testing and deployment pipelines to ensure platform resilience.

Operating at the intersection of computational engineering and healthcare, the engineer will interact with computer scientists, medical engineers, and clinicians. The role involves translating complex clinical and research requirements into robust software specifications, directly facilitating the deployment and prospective evaluation of cutting-edge AI models in clinical workflows. Additionally, the engineer will monitor cluster performance, optimize computational workloads, and provide hands-on technical support and onboarding for researchers and clinical users.

Qualification requirements

The candidate must hold a Master’s degree in Computer Science, Software Engineering, or a related quantitative field. Essential qualifications include strong proficiency in Linux environments and Python programming, proven hands-on experience with Docker and Kubernetes container orchestration, backend/API development (RESTful services and database integration), and standard software development workflows using Git.

Excellent English communication skills and the ability to collaborate effectively in an interdisciplinary environment alongside engineers and clinicians are required.

Meriting qualifications include practical experience with Electronic Health Record (EHR), medical imaging standards (DICOM, NIfTI) and imaging servers (Orthanc, PACS, 3D Slicer), familiarity with medical AI frameworks and MLOps pipelines (PyTorch, MONAI, MLflow, Kubeflow), experience with CI/CD and GitOps tooling (ArgoCD, Ansible), and knowledge of enterprise security protocols (Keycloak, OIDC) for sensitive health data.

About the employment

This is a full-time, fixed-term position for 5 months, with the possibility of extension (up to a total of 7–8 months, depending on project funding). Stockholm University practices individual salary determination. Start date: as soon as possible, by mutual agreement.

Ref. No. SU FV-2829-26

Closing date: 4 October 2026

Complete information here