Interested in this AI/ML Engineer role at The University of Michigan?
Apply Now →About This Role
How to Apply
A cover letter and resume are important submissions for the hiring team to get a sense of your experience. In the cover letter, in two pages or less, please let us know how this role aligns with your career aspirations and skill set. Submit both a cover letter and resume as one file (because of system limitations).
Who We Are
About the Center for Academic Innovation
Through curricular innovation, tools for student success, and educational research and analytics, the University of Michigan Center for Academic Innovation is building the future of education. Our vision is a future in which education connects and empowers learners everywhere to reach their full potential throughout their lives. To realize our vision, we make it our mission to collaborate across campus and around the world to create equitable, lifelong educational opportunities for learners everywhere.
About the Technology Infrastructure and Support Team
The TIS team builds and maintains the technology behind CAI's learning, media, and IT work. Our work spans Michigan Online infrastructure, cloud storage, cybersecurity, disaster recovery, studio production, XR, AV systems, media asset management, and internal support. TIS is also home to the AI Enablement Team, which partners with teams across the Center to test promising ideas and turn the ones that work into tools and workflows people can rely on. Working closely with Creative Studios, Operations, and product engineering teams, we keep CAI's systems dependable and secure so learners, faculty, and staff can focus on their work.
For more information, please visit our website: Academic Innovation.
Responsibilities*
- Lead structured intake and discovery with faculty and staff, map current workflows, and identify the problem, users, constraints, and baseline performance
- Translate validated needs into engagement briefs with scope, owners, success measures, risks, dependencies, and a clear handoff plan
- Plan and lead cross-functional AI enablement engagements from discovery through prototype, evaluation, adoption, and operational handoff
- Build or coordinate prototypes using approved AI tools, APIs, data sources, and workflow platforms
- Define evaluation plans that test quality, reliability, efficiency, accessibility, and user adoption before scaling a solution
- Coordinate privacy, security, data governance, accessibility, procurement, and policy reviews with the appropriate CAI and university partners
- Facilitate working sessions, demonstrations, documentation, and role-specific learning that help teams adopt new workflows responsibly
- Create reusable patterns, templates, and decision records so successful approaches can be applied across CAI
- Track engagement capacity, progress, outcomes, and risks, and provide concise portfolio reporting to the AI Council and TIS leadership
- Guide the work of the Applied AI Engineer (post-graduate fellow), student fellows, and rotating domain experts assigned to engagements
What You'll Do
The Center for Academic Innovation at the University of Michigan is seeking an AI Enablement Lead to turn high-value workflow problems into responsible, measurable AI-enabled solutions. Reporting to the Director of Technology Infrastructure and Support, you will lead engagements from discovery through evaluation, adoption, and operational handoff. You will combine consultative practice, hands-on prototyping, stakeholder facilitation, and disciplined delivery. The successful candidate is pragmatic, collaborative, and able to translate among domain experts, technologists, data and policy partners, and executive sponsors.
Required Qualifications*
- Bachelor's degree or an equivalent combination of education and experience
- Three or more years leading applied technology, product or service design, process improvement, innovation, or consulting projects
- Demonstrated experience designing or building AI-enabled workflows with large language model tools, APIs, or automation platforms
- Experience mapping workflows, facilitating colleagues, turning ambiguous needs into clear requirements and a manageable delivery plan, and supporting teams through the workflow and role changes that come with new ways of working
- Experience setting evaluation criteria and using qualitative and quantitative evidence to decide whether to continue, revise, scale, or stop a project
- Ability to lead several workstreams and cross-functional contributors with clear ownership and follow-through, including presenting progress, results, and recommendations to executives and leadership groups
- Working knowledge of responsible AI, privacy, security, accessibility, and data governance
- Ability to learn and appropriately use new technologies to improve processes, communication, and outcomes, including AI-enabled tools where relevant to the role, consistent with university policy and job responsibilities
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.
Additional Information
This is a 3-year term-limited position with the possibility of renewal depending on funding. The position may be renewed or converted into an open-ended base appointment.
Candidates must have legal authorization to work in the United States.
The mode of work for this position is Hybrid with a minimum of 4 days in the office per week, Monday through Thursday, with an option of remote work on Fridays. On occasion, you may be required and must be available to work onsite on Fridays if necessitated by our center's policy and domain leadership or by your job requirements.
The salary for this position will be based upon the selected candidate's education and experience.
We support the creative, responsible, and ethical use of AI tools to enhance our work, in alignment with institutional policies and best practices.
Excellent benefits are available, for details, see http://benefits.umich.edu/
Salary Information
The general salary range for this position is $90,000 - $96,000. Candidates can expect to be paid a competitive and equitable salary. Factors used to determine salary include education level, experience, knowledge and skills for the position and salaries that are currently established within the unit. All candidates are encouraged to discuss salary questions to honor alignment and transparency throughout the recruiting process.
Organizational Statement on Harassment
We believe all individuals deserve to be treated with respect and dignity and have the right to work in a professional atmosphere that promotes mutual respect and a safe space for collaboration. Any concerns shared with any staff members and student employees in the Center for Academic Innovation will be reported to the Equity, Civil Rights, & Title IX Office to be reviewed and addressed, and the Center for Academic Innovation encourages reporting of any forms of harassment including sexual and gender-based harassment.
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 will be 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.
Decision Making Process
Applications will be reviewed as received throughout the posting period and continue until the position is filled.
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
284288
Working Title
AI Enablement Lead
Job Title
Business Proc Consultant 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
Vp Acad Innovation
Department
Center for Academic Innovation
Posting Begin/End Date
10/13/2026 - 10/27/2026
Salary
$90,000.00 - $96,000.00
Career Interest
Information Technology
Salary Context
This $90K-$96K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($93K) sits 56% below the category median. Disclosed range: $90K to $96K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
The University of Michigan AI Hiring
The University of Michigan has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Ann Arbor, MI, US. Compensation range: $70K - $115K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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