This position is term-limited for 2 years with the possibility of renewal based on need and available funding.  

We are looking for a senior hands-on engineer to work with interdisciplinary teams on the design, delivery, and optimization of research software systems at the intersection of AI and health. These multi-stakeholder projects focus on leveraging machine learning (ML) and AI-enabled tools to support personalized care and efficient healthcare delivery in resource-constrained settings. Projects span both domestic and global settings and involve collaborations across diverse cultural and organizational contexts.

The Applications Systems Analyst/Programmer Senior will architect and operate production-grade data and ML pipelines and integrate models into user-facing applications with partners to secure external funding and translate prototypes into reliable tools for clinical and public-health impact. Projects use multimodal data sources (including EHR, imaging, textual, sensor, and survey data) and AI/ML methods. Many efforts require HPC/cluster execution, orchestration of long-running batch inference, stringent data governance, and usable front ends that surface model outputs to diverse stakeholders in the U.S. and abroad.

This is a full-time exempt position that reports to the Project Senior Manager.

Applications & Modeling

• Build secure, maintainable web applications and APIs (e.g., Python/Django, PostgreSQL) to expose model outputs to researchers, clinicians, and partners
• Create interactive decision-support front ends and rules engines that connect analytics to operational workflows
• Develop and evaluate ML models for imbalanced healthcare problems (e.g., time-series, text) using appropriate metrics and calibration
• Implement anomaly detection, interpretability/reporting pipelines, and experiment tracking for transparent decision making
• Experience in grant development, translating research aims into proposal-ready prototypes and contributing methods, figures, and data management plans with PIs and partners

ML Systems & Infrastructure

• Design, build, and operate reproducible ML pipelines for training and inference (e.g., Snakemake or equivalent) including GPU/CPU scheduling, job queuing, and fault-tolerant I/O on shared storage
• Develop robust ETL and event-driven data flows (e.g., object storage, serverless triggers, logging/monitoring) with strong documentation and reproducibility
• Establish CI/CD practices, version control, containerized environments, and deployment playbooks for research computing
• Proven hands-on health/AI experience delivering end-to-end ML on clinical and sensor data, with interpretability and reliability.
• Engineer and operate HPC ML pipelines by defining reproducible workflow DAGs with a workflow engine, ensuring reliability with failure recovery and checkpointing.

Collaboration & Mentorship

• Partner with faculty on statements of work, timelines, and budgets; contribute methods sections, figures, and demos for grant applications and manuscripts
• Mentor early-career researchers with our international partnerships in data exploration, cleaning, manipulation, and analysis, with an emphasis on reproducible data science practices and adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles to strengthen long-term research capacity
• Frequent collaboration with teams in different time zones, and extended availability beyond standard business hours will be necessary

• Bachelor's degree in Computer Science, a related field, or equivalent experience 
• 5 - 7 years of programming experience 
• Fluency in Python (data analysis, manipulation, processing) and SQL (database development and querying)
• Strong experience with Git and collaborative software development best practices on Linux/Unix; comfort with containerized environments and reproducible workflows
• Familiarity with MLOps/DevOps, including CI/CD pipelines, observability/monitoring, experiment tracking, and deployment of secure, auditable ML systems
• Proven experience applying data science techniques in global or cross-border projects
• Excellent communication (oral and written), organizational skills, and attention to detail

• Completion of a master's degree in computer science, bioinformatics, clinical informatics, information, statistics, computer science, data science, or related fields
• Experience in application development, deep learning/AI packages, and machine learning techniques (including time-series analysis, imbalanced data, and anomaly detection)
• Experience handling sensitive or confidential healthcare data (e.g., HIPAA-regulated records, medical imaging, unstructured clinical text, telemetry/sensor data)
• Exposure to secure ETL pipelines, cloud/object storage, event-driven data flows, and FAIR/reproducible data science practices
• Familiarity with MLOps frameworks (e.g., Weights & Biases) and integrating model interpretability and audit trails into production workflows

The position is full-time and is eligible for flexible (i.e., hybrid) work arrangements. You will be expected to be in person a minimum of 3 days per week. Our offices are located at the North Campus Research Complex. Flexible work agreements are reviewed annually and are subject to change based on the business needs of the hiring department, throughout the course of employment. In addition to the regular in office workdays/hours, there will be additional times, some early morning and/or evening hours that require meetings with collaborators. 

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 is term-limited for 2 years with the possibility of renewal based on need and available funding.  At the end of the stated term, your appointment will terminate and will not be eligible for Reduction-in-Force (RIF) benefits. This term-limited appointment does not create a contract or guarantee of employment for any period of time as you will remain subject to disciplinary or other performance measures, up to and including termination, at the will of the University in accordance with existing University policy and standards for employee performance and conduct.

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 anytime after the minimum posting period has ended.

The University of Michigan is an equal employment opportunity employer.