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About This Role
How to Apply
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.
Job Summary
The mission of the University of Michigan Office of the Vice President for Research (OVPR) is to catalyze, support, and safeguard U-M research. To that end, our team is constantly striving to foster new research, build partnerships, and provide support to our researchers to realize our vision of research to serve the world.
OVPR runs on systems that were never designed to talk to each other. Our small IT team supports more than thirty units, and much of what we build closes that gap: pulling identity, human resources, finance, space, and service data into one place, then replacing manual, spreadsheet driven work with software people actually want to use.
In this role you will design and build those applications. You will write the code, own the data structures behind it, and integrate against university services through the U-M API Gateway and central identity systems. You will also sit with the staff whose process you are automating and ask enough questions to understand it before you build anything. And you will use AI as an engineering tool rather than a talking point, because a growing share of what we ship is built with, or powered by, the university's generative AI platforms.
This is a small team with a wide surface. You will own work early, you will see it in production quickly, and the people using it are the ones supporting research across the university.
Responsibilities*
Application Development and Integration
- Design, build, and maintain web applications and internal tools that consolidate data from across the university, including OVPR's Backstage portal and its custom plugins.
- Develop and consume APIs, including university services reached through the U-M API Gateway such as Finance, Department, MCommunity, and TeamDynamix.
- Integrate applications with central identity services including Okta OIDC and Shibboleth, and with unit systems of record such as CollectionSpace.
- Deploy and support containerized applications on the U-M Container Service.
- Troubleshoot and resolve defects across the full stack, from the interface through to the data layer.
Data Architecture and Management
- Model, build, and query the relational data behind OVPR applications, keeping structures clear enough that a small team can maintain them.
- Aggregate data across sources that frequently disagree, including identity claims, human resources records, financial shortcodes, space and equipment data, and group memberships.
- Build reporting and dashboards that answer the questions OVPR units ask repeatedly.
- Apply appropriate handling for institutional data based on its sensitivity classification.
Process Automation and Applied AI
- Turn documented business processes into working automation, replacing manual and spreadsheet based workflows with supported applications.
- Apply the university's generative AI platforms, including the U-M GPT Toolkit and ITS AI Workflows, to automate multi step work with human review built in.
- Evaluate where AI genuinely improves an outcome and where a conventional solution is the better answer.
- Identify automation opportunities while working alongside unit staff, and raise them rather than waiting to be asked.
Requirements and Collaboration
- Work directly with OVPR unit staff to understand how a process actually runs, not only how it is documented, and translate that into a technical design.
- Partner with ITS and other technical providers on shared services, integrations, and dependencies.
- Explain technical tradeoffs clearly to people without a technical background, and bring them along on the decision.
- Coordinate with the IT Business Consultant and Associate Director so that consultation, project delivery, and development stay aligned.
Technical Practice and Documentation
- Document applications, data structures, and integrations so that a small team can support them over time.
- Contribute to code review, shared standards, and OVPR's version control practice.
- Build and maintain applications in line with web accessibility standards and the university's obligations.
- Stay current on university services and emerging technology, and bring back what is worth adopting.
Required Qualifications*
Bachelor's Degree or Equivalent Experience: A bachelor's degree in computer science, information systems, information technology, or a related field, or an equivalent combination of education, certification, and experience that demonstrates the foundational knowledge required for this role.
Application Development Experience: Two or more years of professional experience building and maintaining web applications, including both interface and server side work, and delivering them to real users.
Database and Data Structure Proficiency: Demonstrated experience with relational databases, including schema design, writing non trivial queries, and reasoning about how data should be structured rather than only consuming it.
Systems Integration and APIs: Hands on experience consuming and building REST APIs, and connecting systems that were not designed to work together, including handling authentication, error conditions, and inconsistent data.
Programming Proficiency: Working proficiency in at least one server side language such as PHP, Python, or JavaScript, with the ability to read and extend an unfamiliar codebase.
Collaborative Development Practice: Experience with version control and collaborative development, such as Git and review based workflows.
Communication and Requirements Gathering: Ability to explain technical work clearly to people who are not technical, and to gather requirements by asking questions that surface what was left unsaid.
Independent Learning: Demonstrated ability to learn unfamiliar technologies quickly and with limited direction.
Desired Qualifications*
University and Enterprise Systems Experience: Experience working within a university or another large, decentralized enterprise environment, with a working understanding of how central services, unit level systems, and institutional policy fit together, and why the same information often lives in several systems at once.
Familiarity with University of Michigan Services: Exposure to U-M systems and services such as the U-M API Gateway, Okta and Shibboleth, MCommunity, TeamDynamix, M-Pathways, or the U-M Container Service.
Applied Generative AI: Experience putting generative AI into production, including large language model APIs, retrieval over institutional content, or agentic workflows, together with sound judgment about where AI belongs and where it does not.
Enterprise AI Platforms: Experience with the U-M GPT Toolkit, ITS AI Workflows, or comparable enterprise AI platforms and their data handling requirements.
Business Process Analysis: Experience documenting, analyzing, and improving business processes, and comfort leading that conversation with the people who own the process.
Higher Education or Research Context: Experience in higher education, research administration, or another environment with meaningful compliance and policy requirements.
Accessibility Awareness: Working knowledge of web accessibility standards, including WCAG conformance and ADA Title II obligations.
Platform and Infrastructure Breadth: Experience with WordPress development, containerization, or infrastructure as code.
Institutional Awareness and Influence: Ability to move work forward across unit boundaries without formal authority, and awareness of how institutional politics shape what is actually possible.
Modes of Work
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.
Background Screening
The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.
Application Deadline
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
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
283639
Working Title
AI Solutions Developer
Job Title
Full Stack Developer Intermed
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
Umor Off Vp Resrch
Department
UMOR Office of VP for Research
Posting Begin/End Date
9/28/2026 - 10/12/2026
Salary
$105,000.00 - $115,000.00
Career Interest
Information Technology
Salary Context
This $105K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $105K to $115K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
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. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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