A cover letter is required for consideration for this position and should be attached as the first page of your resume. The cover letter should address your specific interest in the position and outline skills and experience that directly relate to this position.

About e-HAIL and AI & Digital Health Innovation

AI & Digital Health Innovation represents a unification of interdisciplinary efforts at the intersection of artificial intelligence (AI) and health at the University of Michigan. We leverage community partners to provide forums for sharing digital and health AI research ideas, foster innovation, and strengthen proposal development initiatives. The e-HAIL Community is a supportive community of researchers that interacts regularly through online brainstorming sessions and in-person workshops, primarily focused on helping engineering and medical faculty prepare proposals for grant submissions.

Why Join Michigan Medicine?

Michigan Medicine is one of the largest healthcare complexes in the world and has been the site of many groundbreaking medical and technological advancements since the opening of the U-M Medical School in 1850. Michigan Medicine has over 30,000 employees and our vision is to attract, inspire, and develop outstanding people in medicine, sciences, and healthcare to become one of the world's most distinguished academic health systems. In some way, great or small, every person here helps to advance this world-class institution. Work at Michigan Medicine and become a victor for the greater good.

What Benefits can you Look Forward to?

• Excellent medical, dental and vision coverage effective on your very first day
• 2:1 Match on retirement savings

We are looking for an AI/ML Research Engineer/Programmer to work on interdisciplinary research projects as part of faculty collaborations between the College of Engineering and the Medical School that target external grant applications for our e-HAIL (E-Health and Artificial Intelligence) community.

The e-HAIL AI/ML Research Engineer/Programmer will provide a broad spectrum of technical support using a variety of data sources (including EHR, imaging, textual, sensor, and survey data) and AI/ML methods. Projects will require the design, delivery, and optimization of solutions needed to validate a proof-of-concept before a grant can be submitted. These proof-of-concepts range from new model development to existing model optimization, and tools needed to validate models.

We are seeking a self-motivated, energetic individual with a strong background in Machine Learning and Computer Science, along with proven experience in systems analysis and programming. The ideal candidate will contribute to architecting and operating reproducible data and ML pipelines, integrating models into user-facing applications, and ensuring robust data governance and security practices. You will have the opportunity to work in an exciting and rewarding research area that constantly poses new technical and computational problems and contributes to better health outcomes.

This position is hybrid. A fully remote role would be considered for the right candidate.
 

• ML Systems & Pipelines
• Design, build, and maintain reproducible data and ML pipelines supporting both research experimentation and production deployment, including batch and distributed computing environments.
• Implement robust data engineering practices: ETL processes, event-driven data flows, logging, monitoring, error handling, and fault-tolerant I/O.
• Integrate model interpretability, evaluation, calibration, experiment tracking, and auditability into ML workflows to support transparent and responsible AI in healthcare.
• Application Development
• Develop secure, maintainable web applications, APIs, and interactive interfaces that surface model outputs and decision-support tools to researchers, clinicians, and partners.
• Build and integrate ML models into user-facing systems, ensuring reliability, scalability, and accessibility.
• Research & Collaboration
• Plan and coordinate software development across diverse faculty-led projects, contributing technical expertise from design through deployment.
• Write technical documentation, prepare summary reports and presentations, and provide input on grant applications and research manuscripts.
• Stay current on the latest technologies, tools, and best practices in ML systems, reproducible data science, and software engineering.
• Governance & Compliance
• Ensure compliance with data governance, security, and regulatory requirements (including HIPAA) when working with sensitive healthcare data.
• Follow best practices in version control, code review, containerization, CI/CD, and documentation for research computing.
   

• Bachelor's degree in Computer Science, a related field or equivalent experience.
• 3 - 5 years of industry experience.
• Fluency in Python or other languages for data analysis, manipulation, and processing.
• Fluency in SQL for database development and manipulation.
• Experience designing and maintaining reproducible data and machine learning pipelines for training and inference.
• Proficiency in Linux/Unix environments and familiarity with HPC or distributed computing.
• Experience with modern software engineering practices: version control, containerized environments, and reproducible workflows.
• Understanding of MLOps concepts including CI/CD, experiment tracking, monitoring, and secure ML deployment.
• Experience integrating ML models into user-facing applications or systems.
• Excellent written and verbal communication skills with strong organizational attention to detail.
   

• Master's degree in Computer Science, Bioinformatics, Clinical Informatics, Statistics, Data Science, or a related field.
• Familiarity with ML techniques including time-series analysis, imbalanced data, anomaly detection, and model calibration/evaluation
• Experience with secure, maintainable APIs or analytics tools for decision support.
• Familiarity using medical imaging data and/or unstructured data (e.g., text data).
• Knowledge of basic and advanced statistical techniques and concepts (probability theory, generalized linear models, statistical hypothesis testing).
• Experience with MLOps best practices: experiment tracking, interpretability pipelines, workflow orchestration.
• Experience engineering workflow-based ML pipelines with checkpointing, failure recovery, and scalable compute scheduling.
• Exposure to secure ETL pipelines, object or cloud-based storage systems, and event-driven data architectures.
• Experience working with sensitive and confidential data such as healthcare records regulated by HIPAA, and familiarity with responsible AI, auditability, and data governance principles.
• Familiarity with FAIR (Findable, Accessible, Interoperable, Reusable) data and reproducible data science practices.
   

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

This position may be underfilled at a lower classification depending on the qualifications of the selected candidate.

Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third party administrator to conduct background screenings.  Background screenings are performed in compliance with the Fair Credit Report Act. Pre-employment drug testing applies to all selected candidates, including new or additional faculty and staff appointments, as well as transfers from other U-M campuses.

Job openings are posted for a minimum of seven calendar days. The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled any time after the minimum posting period has ended.

The University of Michigan is an equal employment opportunity employer.