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About This Role
How to Apply
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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.
Who We Are
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AI\&DHI serves as the operational backbone for AI and digital health research at U\-M, spanning faculty, researchers, and clinicians across 75 departments and 17 schools and colleges. Our team provides the data infrastructure, AI tools, systems integrations, and research enablement services that help investigators translate promising ideas into validated, deployable innovations. We partner closely with Michigan Medicine, Michigan Engineering, and other institutional units to deliver services researchers can rely on.
Job Summary
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AI \& Digital Health Innovation (AI\&DHI) is the enabling engine for AI and digital health research at the University of Michigan, providing the infrastructure, tools, and expertise that help researchers move from idea to validated impact. We are seeking a full\-time AI Systems Engineer to design, build, and maintain the systems and integrations that power AI\-driven research and clinical tools across Michigan Medicine.
This role sits at the intersection of data engineering and AI systems development, building robust pipelines, managing OpenShift/Kubernetes\-based infrastructure, and integrating AI tools with platforms such as Epic EHR, frontier/enterprise LLMs, and other services offered throughout the university. We are looking for a self\-motivated engineer with a strong background in systems development, container orchestration, and AI tool deployment who thrives in a collaborative, research\-driven environment.
This position is hybrid, primarily working from home with onsite opportunities in Ann Arbor. A fully remote role would be considered for the right candidate.
Responsibilities\*
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- Design, build, and maintain data pipelines and AI systems that support research and clinical workflows across AI\&DHI initiatives;
- Develop and manage containerized applications and services using Kubernetes (OpenShift), ensuring reliable, scalable deployment in secure research environments;
- Integrate AI tools and research platforms with institutional systems including MiChart, ChatGPT/Claude, REDCap, and other Michigan Medicine infrastructure;
- Architect and maintain backend systems that power AI\-enabled research tools, including PRISM and HELPR LLM Tools;
- Partner with researchers, clinicians, and technical teams to translate requirements into well\-engineered, production\-ready systems;
- Ensure systems meet institutional security, compliance, and data governance standards, including HIPAA requirements for health data;
- Develop clear technical documentation for all systems, pipelines, and integrations;
Required Qualifications\*
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Systems Programmer/Analyst Sr
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- Master's degree in Computer Science, Data Engineering, Biomedical Informatics, or a related field
- 5 to 7 years of systems/software programming activities in a business environment with a comprehensive knowledge of software applications.
- Experience working with EHR platforms, particularly Epic/MiChart integrations
- Familiarity with large language model (LLM) APIs and AI tool deployment
- Experience with Linux/Unix environments for research computing
- Experience working with sensitive and confidential data regulated by HIPAA
- Familiarity with HL7 FHIR standards for health data interoperability
Systems Programmer/Analyst Intermediate
- Bachelor's degree in Computer Science, Software Engineering, a related field, or equivalent experience
- 3\-5 years of experience developing and deploying AI tools and systems
- Hands\-on experience with Kubernetes (OpenShift) for container orchestration and deployment
- Proficiency in Python or equivalent languages for data engineering and systems development
- Experience with cloud\-native infrastructure and DevOps practices, including CI/CD pipelines and version control (Git)
- Experience integrating software systems with external APIs and enterprise platforms
- Strong communication skills, both oral and written, with the ability to work across technical and non\-technical teams
- Strong organizational skills with attention to detail and accuracy
Desired Qualifications\*
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Systems Programmer/Analyst Sr
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- Master's degree in Computer Science, Data Engineering, Biomedical Informatics, or a related field
- Experience working with EHR platforms, particularly Epic/MiChart integrations
- Familiarity with large language model (LLM) APIs and AI tool deployment
- Experience with Linux/Unix environments for research computing
- Experience working with sensitive and confidential data regulated by HIPAA
- Familiarity with HL7 FHIR standards for health data interoperability
Systems Programmer/Analyst Intermediate
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- Master's degree in Computer Science, Data Engineering, Biomedical Informatics, or a related field
- Experience working with EHR platforms, particularly Epic/MiChart integrations
- Familiarity with large language model (LLM) APIs and AI tool deployment
- Experience with Linux/Unix environments for research computing
- Experience working with sensitive and confidential data regulated by HIPAA
- Familiarity with HL7 FHIR standards for health data interoperability
Why Join Michigan Medicine?
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The University of Michigan Medical School (UMMS) fosters the education and research missions of Michigan Medicine, one of the top\-ranked academic medical centers in the country. The school has a 172\-year history of creating innovative programs designed to train the next generation of leaders in medicine, science, and other emerging health professions. Throughout our 20 clinical and nine basic science departments, we are committed to a single mission: to transform health through bold and innovative education, discovery, and service.
What Benefits Can You Look Forward To?
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Excellent medical, dental, and vision coverage effective on your very first day
2:1 match on retirement savings
Modes of Work
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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.
Underfill Statement
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This position may be underfilled at a lower classification depending on the qualifications of the selected candidate.
Background Screening
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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 Reporting 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.
Application Deadline
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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.
U\-M EEO Statement
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The University of Michigan is an Equal Opportunity Employer. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants, including protected veterans and individuals with disabilities.
### Job Opening ID
281248
### Working Title
Data and AI Engineer
### Job Title
Systems Programmer/Analyst Sr
### Work Location
Ann Arbor Campus
Ann Arbor, MI
### Modes of Work
Hybrid
### Full/Part Time
Full\-Time
### Regular/Temporary
Regular
### FLSA Status
Exempt
### Organizational Group
Exec Vp Med Affairs
### Department
MM AI\&DHI
### Posting Begin/End Date
8/05/2026 \- 8/19/2026
### Career Interest
Information Technology
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The University of Michigan, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
The University of Michigan AI Hiring
The University of Michigan has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Ann Arbor, MI, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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