Interested in this AI/ML Engineer role at University of Minnesota?
Apply Now →About This Role
Required Qualifications:
- BA/BS Degree plus at least 12 years of generally relevant experience (can be non\-AI experience), or master’s degree plus 10 years of experience (can be non\-AI experience).
- Leadership experience, including strategic planning and operational oversight.
- Strong technical knowledge of artificial intelligence, including machine learning, generative AI, large language models (LLMs), natural language processing, automation, and AI\-enabled platforms.
- Demonstrated experience in governance, ethics, fairness, transparency, and explainability.
- Ability to navigate complex organizational structures, build support among key stakeholders, and anticipate challenges by understanding institutional dynamics and decision‑making processes.
- Ability to navigate ambiguity, manage conflict, and lead through change.
Preferred Qualifications:
- Experience working within higher education, research institutions, healthcare, government, or similarly complex and distributed environments.
- Demonstrated success leading AI transformation in complex organizations.
The Director for Artificial Intelligence is a key member of the Information Technology leadership team, serving as the University’s senior IT leader responsible for advancing enterprise AI offerings. In strong partnership with the Vice Provost for Artificial Intelligence, who provides institution\-wide academic and strategic leadership, this role translates the University’s AI vision into actionable IT strategy and execution. Reporting to the Vice President for Information Technology and CIO, this role leads the development, delivery, and scaling of AI technology services, platforms, and infrastructure that support the diverse needs of colleges, campuses, and administrative units. The Director partners closely with IT leaders across the system to drive alignment, enable shared capabilities, and accelerate responsible AI adoption at scale. This role focuses on advancing the aggressive pursuit of innovation and enhancing the effectiveness of teaching, research, and administrative functions while balancing the implementation of AI technologies in ways that are secure, reliable, and operationally sustainable.This leader is also responsible for building and maturing AI capabilities across the IT community by fostering a culture of collaboration, experimentation, and continuous learning to ensure the IT community is positioned to adapt and lead in a rapidly evolving AI landscape.
Reports To: Vice President and Chief Information Officer (VP/CIO)
Work Modality: Hybrid; candidates must be located near a University of Minnesota campus, or be open to relocation.
Please note, this position is not eligible for H\-1B or Green Card sponsorship. This position does not offer a STEM OPT training program.
Leadership \& Vision
- Partner with AI Hub leadership to operationalize strategy, translating research, innovation, and academic direction into scalable IT service offerings that align with institutional mission and long‑term goals.
- Translate emerging AI opportunities into actionable roadmaps, integrating academic, research, and administrative priorities.
- Manage strategic investment in AI capabilities across IT, prioritizing scalable platforms, infrastructure, and talent to enable responsible innovation and long\-term institutional impact.
- Unify AI related IT service offerings across the University, establishing a cohesive portfolio of platforms, tools, and support models that reduce fragmentation, and provide consistent, scalable access to AI capabilities for colleges, campuses, and administrative units.
Governance, Policy \& Ethical Stewardship
- Lead the enterprise technology aspect of the University AI governance framework, ensuring transparency, accountability, and ethical use across the university.
- Oversee the development and implementation of AI\-related IT policies, guiding ethical and responsible AI use within the University's IT technology portfolio.
- Develop policies, standards, and procedures for AI usage, including privacy, explainability, fairness, and risk categorization.
- Facilitate a collaborative process to align AI adoption with institutional security and compliance requirements, including data protection and responsible use.
- Engage stakeholders to ensure AI initiatives reflect institutional needs.
Innovation, Use Case Development \& Capability Building
- Support the University’s AI innovation pipeline, including ideation, prioritization, and execution of high‑value AI initiatives.
- Communicate complex AI concepts clearly to audiences ranging from technical experts to non‑technical users.
- Champions a culture of continuous learning by advancing AI education initiatives that build capability, promote responsible use, and accelerate innovation.
- Promote pragmatic and strategic use of AI and an adaptive mindset across the institution.
- Oversee the creation of reusable AI use cases, ensuring scalability, operational readiness, and alignment with university priorities.
Responsible Deployment
- Drive appropriate risk acceptance, articulating risks of non\-action.
- Ensure robust monitoring of AI models, including performance dashboards, drift detection, and compliance reporting.
- Prioritize cybersecurity and data privacy in all AI deployments and partner proactively with the Office of the General Counsel (OGC) and University Information Security (UIS) to negotiate and establish a modernized, agile risk tolerance framework that safely accelerates AI adoption.
The Office of Information Technology (OIT) is the University's central IT unit and provides enterprise\-level technologies and services that are broadly consumed, core to central administrative business operations, and tend to offer substantial economies of scale. We are part of a larger ecosystem of IT professionals who work in academic and support units systemwide. Local or collegiate IT units often offer discipline\-specific, niche, and complementary services to the OIT central services.
Applications must be submitted online. To be considered for this position, please click the Apply button and follow the instructions. The application deadline for this position is July 13th at 11:59pm. You will be given the opportunity to complete an online application for the position and attach a cover letter and resume.
Required application materials: resume
Additional documents may be attached after application by accessing your "My Job Applications" page and uploading documents in the "My Cover Letters and Attachments" section.
To request an accommodation during the application process, please e\-mail [email protected] or call (612\) 624\-8647\.
The University recognizes and values the importance of diversity and inclusion in enriching the employment experience of its employees and in supporting the academic mission. The University is committed to attracting and retaining employees with varying identities and backgrounds.
The University of Minnesota provides equal access to and opportunity in its programs, facilities, and employment without regard to race, color, creed, religion, national origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu
Any offer of employment is contingent upon the successful completion of a background check. Our presumption is that prospective employees are eligible to work here. Criminal convictions do not automatically disqualify finalists from employment.
The University of Minnesota, Twin Cities (UMTC)
The University of Minnesota, Twin Cities (UMTC), is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation's most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world\-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
At the University of Minnesota, we are proud to be recognized by Forbes as a Best Employer for Company Culture (2026\), Best Employer for Women (2023\), and Best Employer by State (2022\-2026\). In 2026, we also received Culture Excellence \& Industry Awards recognition for employee appreciation and work\-life flexibility.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At University of Minnesota, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
University of Minnesota AI Hiring
University of Minnesota has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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