Interested in this AI/ML Engineer role at Intermountain Health?
Apply Now →Skills & Technologies
About This Role
Job Description:
Intermountain Health and Select Health use industry-leading data science and artificial intelligence to deliver on our mission to “Help people have the healthiest lives possible”; making healthcare more affordable and higher quality; and making a difference in the patients and communities it serves.
The Machine Learning Operations Manager is a hands-on and results-oriented leader responsible for overseeing the deployment and monitoring of machine learning products. Specifically, this role supports Machine Learning Operations at Select Health and will consistently deliver measurable reductions in healthcare costs and improvements in member's health while improving the member experience.
This role leads and mentors a talented team of machine learning operations engineers and data engineers who enable deployment and monitoring of enterprise machine learning solutions at scale. This role fosters a culture of excellence, agility, and innovation. Essential Functions
- Accountability for strategic direction of MLOps tech stack.
- Manage AI and ML engineering, data engineering, software engineering, cybersecurity, cloud operations and other team partnerships and strategic alignment with business and technical leaders across the company.
- Ensure efficient application of Responsible AI values to monitor and assess internal and 3rd party machine learning solutions.
- Engage team to execute on AI and ML strategic vision and roadmap.
- Coordinate machine learning operations and data engineering product development and product management.
- Build and train effective machine learning engineering teams, foster employee engagement, ensure best practices, and strategic alignment.
- Mentor and coach machine learning operations engineering leads in developing team culture and project execution.
- Manage and execute the machine learning operations projects portfolio, ensuring timely and quality delivery of solutions that meet the organization's clinical, business, and technical needs.
- Integrate machine learning solutions into key enterprise business processes and decisions, following best practices and frameworks for algorithm governance, ethics, and explainability.
- Keep up to date with industry trends and advancements in machine learning, applying the latest practices and technologies to enhance the organization's capabilities and competitiveness.
- Support innovation by conducting research initiatives and exploring new methodologies and applications of machine learning.
- Recruit, train, and coach machine learning operations personnel, building a strong and skilled team.
Skills
- Machine Learning Operations
- Cloud Platforms
- Artificial Intelligence and Machine Learning Development
- Team Leadership
- Programming and CI/CD Best Practices
Location
This position can be performed remotely with business travel as-needed. Intermountain Health maintains employment registration in Utah, Idaho, Nevada, Colorado, Montana, and Wyoming. Candidates in other locations may be considered. Currently, we are not hiring remote workers in the following states: CA, CT, HI, IL, MA, MN, NY, PA, RI, VT, and WA. Please note that a video interview through Microsoft Teams will be required as well as potential onsite interviews and meetings.
Minimum Qualifications:
- Demonstrated experience of delivering and implementing successful machine learning operations infrastructure and solutions.
- Experience with DevOps best practices such as continuous integration and continuous deployment (CI/CD) and tools such as Docker, Kubernetes, and Git.
- Experience with Infrastructure-as-Code tools such as Terraform and Databricks Asset Bundles.
- Strong leadership skills with the ability to motivate and guide high-performing teams.
- Excellent communication and interpersonal skills, with experience engaging with senior-level stakeholders.
- Solid understanding of machine learning methodologies, algorithms, and frameworks including agentic systems.
- Experience with machine learning tools and programming languages such as Python, R, SQL, etc.
Experience with machine learning production frameworks including Spark, Tensorflow, MLFlow, & LangChain, etc. *
Preferred Qualifications:
- Bachelor's degree in AI, ML, Computer Science, Statistics, or a related field.
- 5 years of experience in machine learning, data science, data engineering, or software engineering
- Experience deploying Generative AI products into operational workflows.
- Experience in health insurance, population health, or related industry with solid understanding of medical economics.
Physical Requirements
Interact with others by effectively communicating, both orally and in writing
Operate computers and other office equipment requiring the ability to move fingers and hands
See and read computer monitors and documents
Remain sitting or standing for long periods of time to perform work on a computer, telephone, or other equipment
May require lifting and transporting objects and office supplies, bending, kneeling and reaching
Additional Information
This is an exempt, full-time position. Pay offers are determined by prior years of relevant experience within the established pay range. In addition to the annual salary, to show our commitment to you and assist with your transition, we may offer a sign-on and relocation bonus when applicable. With this position, you are eligible to participate in the Annual Pay for Performance (AP4P) Plan. This plan enables Intermountain Health to provide leaders with an additional performance compensation opportunity. The AP4P award opportunities are calculated as a percentage of your base salary. Awards are paid out based on attainment of selected Board-approved goals.
Location:
SelectHealth - Murray Work City:
Murray Work State:
Utah Scheduled Weekly Hours:
40 The hourly range for this position is listed below. Actual hourly rate dependent upon experience.
$59.50 - $91.84
We care about your well-being – mind, body, and spirit – which is why we provide our caregivers a generous benefits package that covers a wide range of programs to foster a sustainable culture of wellness that encompasses living healthy, happy, secure, connected, and engaged.
By applying for a position with Intermountain, I acknowledge that I will comply with all applicable Intermountain policies and expectations. If applying for a remote or hybrid role, this includes remote work expectations related to confidentiality, information security, work schedules, conflicts of interest, and use of company equipment. I further acknowledge that outside employment or activities may not interfere with job responsibilities or create a conflict of interest with Intermountain. Actual or reasonably perceived conflicts may be grounds for disqualification from consideration or, if hired, corrective action up to and including termination of employment.
Intermountain Health is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
At Intermountain Health, we use the artificial intelligence ("AI") platform, HiredScore to improve your job application experience. HiredScore helps match your skills and experiences to the best jobs for you. While HiredScore assists in reviewing applications, all final decisions are made by Intermountain personnel to ensure fairness. We protect your privacy and follow strict data protection rules. Your information is safe and used only for recruitment. Thank you for considering a career with us and experiencing our AI-enhanced recruitment process.
All positions subject to close without notice.
Salary Context
This $122K-$189K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Intermountain Health, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($156K) sits 26% below the category median. Disclosed range: $122K to $189K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Intermountain Health AI Hiring
Intermountain Health has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Murray, UT, US, Salt Lake City, UT. Compensation range: $143K - $189K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.