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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*
*Please Note* *: This position requires participation in* *onsite interviews in Salt Lake City, Utah.* *We encourage applicants to apply only if they are comfortable with an in\-person interview process and working closely with cross\-functional teams.*
*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.
\#MachineLearningEngineer
\#MLEngineering
\#MLPlatform
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 *85298*
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
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At University of Utah, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $203,000 based on 85 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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 MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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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