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 Online Learning at CAI
The Online Learning group within CAI consists of four teams that collaborate with others across the Center and University to design and develop online learning experiences that support the future of education for a global community. We work with faculty and academic unit leaders to envision how they can expand reach and impact, advance innovative teaching, and support greater access and lifelong learning through the design and development of integrated online learning experiences. Our teams focus on the following areas:
- Strategic sourcing of new opportunities for online learning;
- Developing internal and external partnerships that promote engagement with learning experiences;
- Providing program management services throughout degree program development and operations;
- Supporting end-to-end design and development of online learning experiences, including design management, learning experience design, and iteration of learning experiences after they have launched;
- Supporting learners' success and engagement in live courses and programs
For more information, please visit our website: Academic Innovation.
Responsibilities*
Innovative, AI, and innovative Technology Support (40%)
- Collaborate with faculty to design, implement, and evaluate funded AI and innovative technology initiatives
- Coordinate CAI's Learning Experience Design support to ensure funded projects integrate effective teaching and learning practices
- Propose technical solutions to pedagogical design challenges through the thoughtful integration of educational technologies
- Work with faculty to identify desired success metrics and provide recommendations for measuring instructional impact
- Establish evaluation criteria that communicate success with projects with innovative technologies
- Maintain strong partnerships with faculty across multiple projects and units
- Provide insight and recommendations for future rounds of support for innovative technologies
- Collaborate with CAI Compliance colleagues to document best practices, ethical considerations, and responsible use frameworks
- Assist in developing workshops, presentations, and resources that enhance AI literacy among educators and learners
- Contribute to evaluation and dissemination of innovative technology pilot outcomes, including white papers and case studies
- Serve as an internal resource on the intentional and responsible use of AI and innovative technologies in teaching and learning
- Work collaboratively with faculty and CAI staff on AI teaching and learning projects, which may include design thinking, rapid prototyping and the design or development of chatbots, agents or learning tools as well as exploring new technologies and informing decisions for future use
Online Course Design (60%)
- Collaborate with faculty, program leaders, Learning Experience Designers, Design Managers, and Media Designers at the Center for Academic Innovation (CAI) to design high-quality online and hybrid courses
- Apply a range of instructional design methods and learning theories to learner-centered course design
- Collaborate with program administrators, faculty partners, and CAI staff to scope the goals of new and iterative course design projects for online courses
- Create and use learner personas for primary and secondary audiences, ensuring that diverse groups of learners are represented
- Provide course design teams and faculty partners with practical examples to spark ideas and creative solutions
- Support integration of practices that follow regulatory and compliance norms for non-credit and credit-bearing courses, including practices related to credit hour guidance, regular and substantive interaction, and accessibility
- Work with academic unit support staff and CAI team members to develop and/or remediate course materials, including media, to make them more accessible for all students
- Refine and apply templates, checklists, and guidelines to assist faculty in the development and iteration of online and hybrid learning experiences
- Collaborate with Design Managers (DMs) to coordinate and manage project pace and scope
- Explore and integrate existing and new educational tools to assess their potential fit within a given project
- Review course design decisions to ensure that activities and resources are aligned with programmatic goals and desired course outcomes
- Use feedback to course design teams on consistency and appropriateness of course content and presentation
- Build content, activities, and assessments on hosting platforms, including the technical integration of learning technology interoperability (LTI) and other third-party educational technology tools
- Characterize the pedagogical design of a course and make research and evidence-based recommendations for immediate iteration and long-term improvement
- Support the Learning Experience Design team in assessing design proposals for innovative online credit-bearing learning experiences to assess feasibility and fit
- Document course design decisions and support hand-off of course development notes to ensure seamless support and iteration of projects
- Explores innovative trends in pedagogy, learning design, and digital innovation, such as generative AI, evaluating their implications for higher education and identifying applications that align with the Center's commitment to excellence and access
What You'll Do
The Center for Academic Innovation at the University of Michigan is seeking a qualified Learning Experience Designer of AI and Innovative Technologies to support the design and iteration of a growing portfolio of technical online and hybrid learning initiatives. You will support academic units in developing both non-credit and for-credit online and hybrid learning experiences. In addition to core design responsibilities, this role will support enhancing CAI's internal capacity for the intentional, responsible and pedagogically sound integration of AI and innovative educational technologies into courses and faculty-led projects.
With a focus on learner-centered design, this role will partner with design team colleagues and external partners to apply established practices and learning theories to the development of high-quality online courses. You will report to the Associate Director of Learning Experience Design and also work with the Director of the EdTech Accelerator.
Required Qualifications*
- Bachelor's degree in education, curriculum and instruction, learning sciences, instructional design, educational technology, or information science or an equivalent combination of education and experience
- 1-2 years of experience (or an equivalent combination of education and relevant experience) in instructional computing, educational technology or instructional design
- Strong collaboration skills in a matrixed environment
- Knowledge of tools for digital avatars, virtual and extended reality, custom chatbots, and AI modeling
- Exhibits a forward-thinking mindset by investigating innovative developments in learning innovation, such as generative AI, evaluating their potential, and identifying strategic opportunities for integration
- Experience in leveraging the use of AI and innovative technologies to most effectively support course goals and learning objectives
- Working knowledge of standards for online non-credit and for-credit course design, including copyright and accessibility
- Understanding of integrating learning theories, instructional design methods, and learner-centered design principles into online learning experiences
- Demonstrated expertise in applying a learner-centered approach to online course design
- Demonstrated expertise in assessing technical project outcomes and making recommendations for improvement and iteration
- Experience using online education platforms (e.g., Canvas, Coursera, and edX)
- Demonstrated initiative and customer service-oriented attitude
- 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 5 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 primary work location will be at 317 Maynard Street, Ann Arbor, Michigan.
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/.
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.
Salary Information
The general salary range for this position is $65,000 - $70,000 You can expect to be paid a great compensation package. Factors used to determine salary include education level and experience for the position and internal equity within the unit. You are encouraged to discuss salary questions to honor agreement and transparency throughout the recruiting process.
Union Affiliation
This position is included in the bargaining unit represented by University Staff United, AFT Local 284, AFL-CIO, which represents Employees in all matters with respect to wages, benefits, hours, and other terms and conditions of employment.
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
284110
Working Title
Learning Experience Designer of AI and Innovative Technologies
Job Title
INSTRUCTIONAL LEARNING INTER
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
10/09/2026 - 11/08/2026
Salary
$65,000.00 - $70,000.00
Career Interest
University Staff United (USU)
Salary Context
This $65K-$70K 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($67K) sits 68% below the category median. Disclosed range: $65K to $70K.
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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