Data Architect Senior

The Machine Learning in Neurosurgery (MLiNS) Lab at the University of Michigan is recruiting a Data Scientist / Machine Learning Engineer to build and lead the multimodal data infrastructure behind our next generation of medical AI systems. You will work with health-system data spanning radiology, digital pathology, intraoperative microscopy, and longitudinal electronic medical records, transforming complex clinical data into reliable, governed, and reusable scientific infrastructure.

This is a high-ownership role for an engineer-scientist who wants to work at the boundary of machine learning, clinical medicine, and large-scale biomedical data. Your work will directly enable foundation models, vision-language systems, and AI agents designed to improve diagnosis, surgical decision-making, and patient care.

• Design and maintain scalable pipelines for ingesting, harmonizing, linking, and versioning multimodal clinical data across radiology, digital pathology, intraoperative imaging, and electronic medical records.
• Develop durable SQL data models, metadata standards, cohort-building tools, and interfaces that connect imaging, pathology, clinical text, procedures, treatments, and longitudinal outcomes.
• Create robust preprocessing pipelines for DICOM studies, volumetric MRI and CT, whole-slide pathology, and stimulated Raman histology.
• Establish automated data-quality monitoring, validation, lineage, provenance, de-identification, and audit processes for HIPAA-regulated research environments.
• Support distributed model training and evaluation on high-performance computing and cloud infrastructure using reproducible environments and modern MLOps practices.
• Contribute to medical foundation models, vision-language models, clinical NLP systems, and AI agents that operate over multimodal health-system data.
• Collaborate with clinicians, scientists, trainees, and engineers, and contribute to manuscripts, datasets, open-source software, conference presentations, and high-impact publications. Opportunities exist to lead independent technical and scientific projects

• Master's degree or higher in computer science, data science, computer engineering, biomedical engineering, bioinformatics, informatics, or a related field. Candidates with substantial equivalent professional experience are also encouraged to apply if permitted by the University job classification.

• Strong Python and SQL skills, with experience building production-quality data pipelines, databases, or scientific software.

• Fluency with Linux, Bash, Git, testing, debugging, documentation, and collaborative software-development practices.

• Experience working with large, heterogeneous datasets and designing reliable, maintainable, and reproducible systems.

• Experience with high-performance, distributed, or cloud computing; familiarity with SLURM is strongly valued.

• Ability to work independently and collaborate across disciplines, with a strong commitment to scientific rigor, data stewardship, responsible AI, and clear communication.

• Experience with DICOM, PACS, whole-slide imaging clinical data warehouses, medical imaging, computational pathology, clinical NLP, or longitudinal EHR data.

• Experience with PyTorch and self-supervised learning, vision-language modeling, large language models, or foundation models.

• Experience with scalable data technologies such as Spark, Dask, Ray, dbt, Airflow, Prefect, or comparable systems.