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About This Role
Overview
The Chief Innovation Office (CIO) at University of Utah Health (U of U Health) would serve as the strategic hub for advancing healthcare innovation, digital transformation, research commercialization, and new models of patient care. The office would connect clinical operations, research, education, and technology to accelerate the delivery of innovative solutions that improve health outcomes, patient experiences, and operational efficiency.
- Senior Machine Learning Engineers leverage their engineering expertise to solve a variety of technical problems for some of the most challenging and impactful projects in healthcare informatics and machine learning.
- You will work on a specific project critical to our needs with the opportunity to switch teams and projects as you and our fast\-paced business grow. We need machine learning engineers who are versatile, rigorous, display leadership qualities and are enthusiastic to take on new problems.
- You will design, develop, test, deploy, maintain and enhance machine learning and AI solutions.
- You will be part of the Innovation Office, a product factory inside the health system of the University of Utah.
Location : 102 Tower (Downtown SLC)
Schedule : Monday \- Friday 9 AM \- 5 PM
Benefits :
- Save 50% on Tuition (Tuition reduced for eligible employees attending the University of Utah)
- Health Coverage, Dental Coverage, Life Insurance
- Retirement
- Paid Time Off
- 11 Paid Holidays per year
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.
*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*
*We encourage candidates to submit resumes that accurately reflect their own skills, experience, and accomplishments. The information provided during the application process must be truthful and authentic, as qualifications may be verified during the hiring process.*
Responsibilities
Essential Functions
- Creating, managing, maintaining, and refactoring ML codebases, pipelines, and workflows for the innovation office.
- Collaborating closely with research, medical, and engineering staff to design and implement ML approaches.
- Implementing scalable ML methods and workflows for high\-performance computing (HPC) resources, in close collaboration with research staff and technical staff.
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Troubleshooting data analysis issues, including implementation issues, hyper\-parameter choices and modeling decision.
- Assisting in preparation of manuscripts and dissemination of results in the appropriate venues.
- Conducing tasks independently and communicate optimally to team members and stakeholders.
- Developing deployable solutions for the health care system.
Knowledge / Skills / Abilities
- Hands\-on coding in C\+\+, Python, PyTorch, or Tensorflow.
- Foundational understanding of supervised and unsupervised learning, reinforcement learning, and machine learning.
- Independently execute in the face of ambiguity
- Leads identifications of dependencies and the development of design documents for a product, application, service, or platform.
- Write efficient systems code and tests and able to debug distributed systems.
- Work on\-call to monitor systems/product/service for degradation, downtime or interruptions.
- Innovative mindset with a keen eye for identifying opportunities for improvement.
- Ability to thrive in a fast\-paced, dynamic environment and simultaneously handle multiple projects.
- Partner effectively with other engineers, product managers, and stakeholders.
Qualifications
Required
- Bachelor’s degree in a relevant field.
- 5 years of experience in software engineering.
Qualifications (Preferred)
Preferred
- Experience working in complex academic medical center environments.
- Experience with large multimodal health datasets.
- Experience with lifecycle management in a fast\-paced software environment.
- Experience in shipping products and scalable, reliable services.
- Hands on experience with asynchronous programming and concurrency (threads, tasks, futures, async/await).
- Experience with Azure Kubernetes Service (AKS), Amazon Elastic Kubernetes Service (EKS), and/or Google Kubernetes Engine (GKE) ‘
- Experience in building database engines, query engines, indexing solutions (columnar, full\-text, vector), at scale.
- Experience with programming CUDA, AI systems at scale.
- Experience with live site operations, Site Reliability Engineering (SRE) or production support roles.
- Experience in networking, distributed systems, lower\-level infrastructure.
- Experience developing accessible technologies.
- Proficiency in code and system health, diagnosis and resolution, and software test engineering.
- Experience with AI agents.
Working Conditions and Physical Demands
*Employee must be able to meet the following requirements with or without an accommodation.*
- This is a sedentary position 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, 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 *84859*
Reg/Temp *Regular*
Employment Type *Full\-Time*
Shift *Day*
Work Schedule *Mon\-Fri 9\-5*
Clinical/Non\-Clinical Status *Non\-Clinical*
Location Name *Information Technology Services*
Workplace Set Up *On\-site*
*City* *SALT LAKE CITY*
*State* *UT*
Department *COR AFF 98A SVP OFFICE TRNSFRS*
Category *Information Technology*
Workplace Set Up *On\-site*
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 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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 Utah AI Hiring
University of Utah has 4 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Research Scientist. Based in Salt Lake City, UT, US. Compensation range: $41K - $125K.
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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