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Overview
*As a patient\-focused organization, University of Utah Health exists to enhance the health and well\-being of people through patient care, research and education. Success in this mission requires a culture of collaboration, excellence, leadership, and respect. University of Utah Health seeks staff that are committed to the values of compassion, collaboration, innovation, responsibility, integrity, quality and trust that are integral to our mission. EO/AA*
This position provides program management and operational coordination for the AI Strategy and Enablement Office within Information Technology Services (ITS) at UHealth. Reporting to the Senior Director of AI Strategy and Enablement, this role is accountable for tracking the progress, operations, and engagements of the enterprise AI program portfolio, including milestone tracking for key enterprise initiatives, coordination of customer and stakeholder engagements, and day\-to\-day operational coordination of the Enablement Office and its cross\-functional efforts. The incumbent serves as a central point of coordination between the Enablement Office, teams, governance bodies, vendors, and operational stakeholders, ensuring initiatives move forward on schedule, decisions and actions are captured and closed, and customer needs and gap opportunities are visible to leadership.
Department Overview: Information Technology Services (ITS) strives to provide industry leading information technology solutions in support of patients, providers, researchers, staff and visitors. The AI Strategy and Enablement Office, within the Office of the Chief Digital and Information Officer, is responsible for enterprise AI strategy, governance alignment, intake and triage, literacy and training, and value realization across UUH. This position has no responsibility for providing care to patients.
Corporate Overview: University of Utah Health is an integrated academic healthcare system with five hospitals including a level 1 trauma center, eleven community health centers, over 1,600 providers, and a health plan serving over 200,000 members. University of Utah Health is nationally ranked and recognized for our academic research, quality standards and overall patient experience. In addition to our clinical delivery system, we have a School of Medicine, School of Dentistry, College of Nursing, College of Pharmacy, and College of Health providing education and training for over 1,250 providers annually. We have over 2 million patient visits annually and research grants exceeding $350 million. University of Utah Hospitals and Clinics represents our clinical operations for the larger health system.
Responsibilities
Essential Functions
Program \& Initiative Tracking
- Tracks milestones, dependencies, risks, and status for key enterprise AI initiatives across the Enablement Office portfolio.
- Maintains the AI initiative portfolio record, including intake status, triage disposition, active engagements, and outcomes.
- Prepares recurring status summaries, dashboards, and leadership\-ready reporting on program progress and portfolio health.
- Identifies schedule, resource, and dependency risks early and escalates with recommended options.
Customer \& Stakeholder Engagement
- Serves as a primary coordination point for customer engagements, ensuring requests are acknowledged, routed, and closed with follow\-through.
- Tracks customer needs and gap opportunities surfaced through engagements and ensures they are captured in the intake and portfolio process.
- Coordinates engagement logistics across departments, clinical and business units, and external partners.
- Supports communication of program updates, decisions, and offerings to stakeholder audiences.
Product \& Vendor Engagement
- Coordinates product engagement activity across enterprise AI platforms and tools, including demonstrations, pilots, feedback collection, and adoption tracking.
- Supports vendor coordination for the Enablement Office, including scheduling, materials, and action follow\-up.
- Tracks utilization and adoption signals across AI tooling to inform enablement priorities.
Meeting Leadership \& Team Coordination
- Coordinates calendars and scheduling for identified initiatives, governance touchpoints, and cross\-functional workgroups.
- Builds agendas, leads and facilitates program meetings, records decisions and action items, and drives follow\-through to completion.
- Coordinates efforts across ITS teams, the Analytics and Insights Office, security, applications, and operational partners in support of AI initiatives.
- Provides overall team coordination for the Enablement Office, keeping priorities, commitments, purchasing and deliverables visible and on track.
Program Operations
- Provides operational coordination for the Enablement Office, purchasing (PO issuance/PCard), including scheduling, document and artifact management, shared workspace upkeep, and meeting logistics.
- Manage team templates, trackers, and standard work that support consistent program execution.
- Supports preparation of executive materials, briefings, and governance committee packets.
Knowledge / Skills / Abilities
- Strong program and project coordination skills, including milestone tracking, action management, and status reporting.
- Excellent organizational skills with demonstrated ability to manage multiple concurrent initiatives and competing priorities.
- Strong written and verbal communication skills, including preparation of concise, executive\-ready summaries.
- Ability to lead and facilitate meetings with participants across organizational levels.
- Ability to influence and coordinate across a matrixed organization without direct authority.
- Working knowledge of program and portfolio tracking tools (e.g., project management platforms, dashboards, collaboration suites).
- Familiarity with, or strong interest in, enterprise AI concepts, tools, and responsible\-use considerations; ability to learn the AI enablement domain quickly.
- Sound judgment regarding confidentiality, escalation, and appropriate handling of sensitive information.
Qualifications
Required
- Bachelor’s degree in Business Administration, Information Systems, Health Administration, Communications, or a related field, or equivalent combination of education and experience.
- Four years of progressively responsible experience in project management, or program operations, preferably supporting technology or enterprise initiatives.
- Experience tracking initiatives, milestones, and deliverables across multiple stakeholders and teams.
- Experience coordinating meetings, calendars, and cross\-functional efforts, including agenda development and action tracking.
Qualifications (Preferred)
Preferred
- Experience in a large healthcare system, academic medical center, or complex matrixed organization.
- Experience supporting an executive office, center of excellence, or enterprise program office.
- Project management certification (e.g., CAPM, PMP) or formal training in program/project management methods.
- Experience with intake, portfolio management, or governance support processes.
- Exposure to AI, analytics, or digital transformation programs.
Working Conditions and Physical Demands
*Employee must be able to meet the following requirements with or without an accommodation.*
- This is a sedentary position in an office setting that may exert up to 10 pounds and may lift, carry, push, pull or otherwise move objects. This position involves sitting most of the time and is not exposed to adverse environmental conditions.
Physical Requirements
Carrying, Climbing, Color Determination, Crawling, Far Vision, Lifting, Listening, Manual Dexterity, Near Vision, Non Indicated, Pulling and/or Pushing, Reaching, Sitting, Speaking, Standing, Stooping and Crouching, Tasting or Smelling, Walking
Multi\-lingual Candidates Welcomed
*To inquire about this posting, email: [email protected]*
EEO Statement
*University of Utah Health Hospitals and Clinics, a part of The University of Utah, values candidates who have experience working in settings with students and patients from all backgrounds and possess a strong commitment to improving access to higher education and quality healthcare for historically underrepresented students and patient populations.*
*All qualified individuals are encouraged to apply. Veterans’ preference is extended to qualified applicants, upon request and consistent with University policy and Utah state law. Upon request, reasonable accommodations in the application process will be provided to individuals with disabilities.*
*University of Utah Health Hospitals and Clinics, a part of The University of Utah, is an Affirmative Action/Equal Opportunity employer and does not discriminate based upon race, ethnicity, color, religion, national origin, age, disability, sex, sexual orientation, gender, gender identity, gender expression, pregnancy, pregnancy\-related conditions, genetic information, or protected veteran's status. The University does not discriminate on the basis of sex in the education program or activity that it operates, as required by Title IX and 34 CFR part 106\. The requirement not to discriminate in education programs or activities extends to admission and employment. Inquiries about the application of Title IX and its regulations may be referred to the Title IX Coordinator, to the Department of Education, Office for Civil Rights, or both.*
*To request a reasonable accommodation for a disability, please contact the University of Utah Health Hospitals and Clinics Human Resources office at 801\-581\-6500\. If you or someone you know has experienced discrimination or sexual misconduct including sexual harassment, you may contact the Director/Title IX Coordinator in the Office of Equal Opportunity (OEO). More information, including the Director/Title IX Coordinator's office address, electronic mail address, and telephone number can be located at:www.utah.edu/nondiscrimination/*
*Online reports may be submitted atoeo.utah.edu/*
*The University is a participating employer with Utah Retirement Systems (“URS”). Eligible new hires with prior URS service, may elect to enroll in URS if they make the election before they become eligible for retirement (usually the first day of work). Contact Hospitals and Clinics Human Resources at (801\) 581\-6500 for information. Individuals who previously retired and are receiving monthly retirement benefits from URS are subject to URS’ post\-retirement rules and restrictions. Please contact Utah Retirement Systems at (801\) 366\-7770 or (800\) 695\-4877 or Hospitals and Clinics Human Resources at (801\) 581\-6500 if you have questions regarding the post\-retirement rules.*
*This position may require the successful completion of a criminal background check and/or drug screen.*
Requisition Number *85146*
Reg/Temp *Regular*
Employment Type *Full\-Time*
Shift *Day*
Work Schedule *8\-5*
Clinical/Non\-Clinical Status *Non\-Clinical*
Location Name *Information Technology Services*
Workplace Set Up *Hybrid*
*City* *SALT LAKE CITY*
*State* *UT*
Department *COS ISC 17A ITS AI SERVICES*
Category *Information Technology*
Workplace Set Up *Hybrid*
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 University of Utah, 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 $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.
University of Utah AI Hiring
University of Utah has 6 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, Research Scientist, Data Scientist. Based in Salt Lake City, UT, US. Compensation range: $105K - $135K.
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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