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About This Role
Healthcare’s helping hand.
CHG shook things up in 1979 by inventing the locum tenens staffing model. We connect doctors with patients who need their care. As the largest physician staffing firm in America, our providers treat millions of patients each year.
Our industry is growing and demand is high. This means you’ll have plenty of opportunities to grow and develop in your career. Keeping healthcare healthy can be as fun as it is rewarding
CareerMD serves physicians, residents, fellows, and healthcare employers through career events, a job board, a candidate database, and recruitment marketing. Our next chapter is intelligence: using AI to compound the leverage of every team inside the company and, over time, to deliver a career\-long opportunity and intelligence system for physicians and the employers who want to reach them.
As our Founding AI Product Engineer, you will identify where AI can measurably improve how CareerMD operates and how its users experience the product. The early work is most likely internal, starting with the teams where operational bottlenecks are clearest, and expands to physician\- and employer\-facing intelligence as internal wins prove out the operating model.
You will work directly with the VP of Engineering and the Product team. This is a high\-ownership, highleverage role where you will discover, build, eval, ship, learn, and iterate.
Responsibilities:
- Discover and automate high\-leverage internal workflows
- Ship AI capabilities end\-to\-end with evals as a first\-class practice
- Partner on data, identity, and architecture
- Build reusable patterns so each capability compounds
- Extend to physician\- and employer\-facing intelligence as internal wins compound
Qualifications:
- Strong software engineering fundamentals; you can design and ship systems end\-to\-end
- Experience building and deploying real\-world AI applications with LLMs, retrieval, embeddings, and agentic patterns
- Experience building and running eval frameworks for AI products
- Ability to identify high\-leverage workflows through direct stakeholder interviews, not just backlog tickets
- Product mindset: you think in terms of user and business outcomes, not just technical output
Education \& Years of Experience:
- 5\+ years of software development
- 3\+ years shipping user\-facing software with direct product ownership
- 1\+ year of shipping AI\-powered applications to production
- Bachelor’s Degree in Computer Science or related work experience
Preferred Skills:
- Shipping AI\-native features into production with measurable outcomes
- Building recommendation, ranking, or matching systems and the feedback loops that improve them
- Designing systems that incorporate user behavior and feedback data •
- Experience in early\-stage or high\-ownership environments where the highest\-value work is not always obvious
*We believe in fair compensation for all of our people, which is why our pay structure takes into account the cost of labor across U.S. geographic markets. For this position, we offer a pay range of* *$* *129,000 \- $193,500* *annually, with pay varying depending on work location and job\-related factors such as knowledge, position level and experience. During the hiring process, your recruiter can provide more information about the specific salary range for the job location.*
*CHG Healthcare offers starting salaries for sales positions in the form of total target compensation (TTC \= base \+ commission \+ bonus), which includes base pay, commission, and bonuses. Sales positions receive short\-term incentives through commission plans and bonuses. On the other hand, non\-sales positions have starting salaries that consist of a base salary and short\-term incentives through various bonus plans, which are paid out monthly, quarterly, or annually.*
*\#LI\-GR1*
In return we offer:
- 401(k) retirement plan with company match
- Traditional healthcare benefits such as medical and dental coverage, and some unique benefits like onsite health centers, corporate wellness programs, and free behavioral health appointments.
- Flexible work schedules \- including work\-from\-home options available
- Recognition programs with rewards including trips, cash, and paid time off
- Family\-friendly benefits including paid parental leave, fertility coverage, adoption assistance, and marriage counseling
- Tailored training resources including free LinkedIn learning courses
- Volunteer time off and employee\-driven matching grants
- Tuition reimbursement programs
Click here to learn more about our company and culture.
CHG Healthcare values a diverse and inclusive workforce. Interested in this role but not a perfect fit? Apply anyway.
We welcome applicants of any race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status and individuals with disabilities as an Affirmative Action/Equal Opportunity Employer. We are an at\-will employer.
What makes CHG Different?
Salary Context
This $129K-$193K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CHG Healthcare, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($161K) sits 25% below the category median. Disclosed range: $129K to $193K.
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.
CHG Healthcare AI Hiring
CHG Healthcare has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US. Compensation range: $193K - $193K.
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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