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About This Role
At UnitedHealthcare, we're simplifying the health care experience, creating healthier communities and removing barriers to quality care. The work you do here impacts the lives of millions of people for the better. Come build the health care system of tomorrow, making it more responsive, affordable and optimized. Ready to make a difference? Join us to start Caring. Connecting. Growing together
UnitedHealthcare is leveraging AI, real\-time data and a modern platform to modernize clinical programs \- starting with Utilization Management. The Medical Director, Clinical AI Innovation and Performance is a senior physician leader responsible for the clinical AI model training, testing and surveillance. Work will initially focus on utilization management (UM) clinical criteria\-leveraging InterQual, UHC custom specialty content and other evidence\-based criteria. The MD will establish efficient and consistent annotation, training and testing models to ensure medical policies are accurately interpreted, evidence based, consistently applied, and operationally effective,
This role serves as the clinical authority for guideline interpretation, clarification, and enhancement, ensuring that approved medical policy and supporting evidence are translated into UM clinical criteria that enable appropriate coverage decisions, support safe and effective care, and scale across enterprise operations. The initial focus will be on Prior Authorization and will expand to include Inpatient Level of Care. Clinical Care Management and Comprehensive Medication Management.
The clinical annotation, training and testing team will report to this physician. The MD will be the primary clinical relationship to Foundation model clinical teams (Antrhopic, OpenAI, Gemini). The CMO will work closely with the CMO Medical Policy, the CMO Value Creation, the CMO of Medical Management and the MD, Data Science. MD will also be a key member of the UCS clinical and AI leadership team, collaborating on clinical program design, data models, AI use and modernization.
You'll enjoy the flexibility to work remotely \* from anywhere within the U.S. as you take on some tough challenges.
Primary Responsibilities:
- InterQual Expertise, Interpretation \& Guideline Enhancement
+ Maintain deep expertise in InterQual criteria, evidence framework, and decision logic
+ Ensure consistent, accurate interpretation of InterQual criteria across:
- Services, settings, and levels of care
- Reviewer types and UM workflows
+ Lead guideline clarification, customization, and enhancement where needed to:
- Improve clinical clarity and consistency
- Address operational ambiguity or recurring edge cases
- Enhance the ability to ensure care is safe and effective
+ Develop and approve evidence based enhancements or supplemental criteria that add clinical clarity while remaining aligned with approved medical policy
+ Work closely with leaders in Medical Policy and evidence teams to ensure:
- Alignment with policy intent
- Clear translation of policy into UM ready clinical criteria
- Evidence Based UM Clinical Criteria
+ Ensure UM clinical criteria are:
- Explicitly grounded in clinical evidence and accepted guidelines
- Structured for consistent application at scale
- Clear and actionable for nurses, physician reviewers, and AI enabled workflows
+ Apply senior clinical judgment in situations where:
- Evidence is evolving or limited
- Criteria require interpretation to ensure appropriate coverage decisions
+ Promote consistent clinical standards across UM operations while recognizing the need for appropriate clinical flexibility
- UM Operations \& Coverage Interpretation Leadership
+ Ensure UM criteria function effectively in production environments:
- Supporting timely and appropriate coverage decisions
- Reducing unwarranted variation across reviewers
- Aligning clinical interpretation with operational reality
+ Partner closely with UM Operations and Nursing leadership to:
- Identify areas of confusion or friction in criteria application
- Address recurring themes from operational feedback
- Improve reviewer confidence and consistency
- Clinical Annotation \& LLM Training for UM
+ Lead teams responsible for clinical annotation and analysis supporting LLM based UM decision support
+ Oversee the clinical review process that ensures:
- Accurate interpretation of UM criteria by LLMs
- Reliable assessment of whether clinical documentation meets criteria
+ Provide clinical oversight and judgment where:
- Annotator disagreement exists
- Criteria vary in complexity or trainability
- Additional training data or clarification is required
+ Ensure annotation and training processes support clinically accurate, reliable model performance
- AI Lifecycle Participation \& Governance (UCS Led)
+ Serve as a senior physician leader within UCS led AI governance, under the authority of the UHC CMO
+ Participate in reviews across AI lifecycle phases:
- Training and validation
- Shadow mode
- AI assist (clinical in the loop)
- Production deployment
+ Contribute clinical judgment to evaluations of:
- Precision and recall
- Clinical accuracy and safety
- Readiness for use in automated approvals
+ Identify areas where additional training, refinement, or safeguards are required
- Cross Functional Clinical Leadership \& Continuous Improvement
+ Directly manage:
- Clinical annotators
- Clinical or UM analysts supporting AI enablement and criteria oversight
+ Work closely with:
- Medical Policy leadership
- CARES team conducting rigorous UM case review and quality evaluation
- UM Operations and Appeals
- Product, ECS, and AI / data science teams
+ Ensure insights from production, appeals, audits, and quality reviews inform:
- Guideline clarification and enhancement
- Reviewer guidance
- Ongoing improvement of AI model performance and safety
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:
- MD or DO with active/unrestricted U.S. Medical Licensure
- Board certification in an ABMS/AOBMS Clinical specialty
- Significant experience in:
- Utilization management
- Coverage interpretation using UM clinical criteria
- Clinical operations in large, complex organizations
- Demonstrated experience working with InterQual clinical criteria
- Experience with UM physicians and nursing teams at scale
- Proven ability to lead through influence across clinical, operational, and technical teams
Preferred Qualifications:
- Experience across Commercial and/or Government program UM
- Experience working with MCG guidelines
- Proven direct responsibility for UM operations leadership
- Proven exposure to AI enabled clinical workflows or advanced decision support (technical expertise not required)
Leadership Profile (Senior Leadership Team Level)
- Recognized clinical authority with strong peer credibility
- Demonstrated judgment in complex or ambiguous clinical scenarios
- Able to operate at enterprise scale while maintaining clinical rigor
- Comfortable providing clinical guidance on AI use in high impact workflows
- Trusted advisor to executive leadership across UCS and UHC
- All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far\-reaching choice of benefits and incentives. The salary for this role will range from $386,500 \- $579,500 annually based on full\-time employment. We comply with all minimum wage laws as applicable."
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
*At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone\-of every race, gender, sexuality, age, location and income\-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes \- an enterprise priority reflected in our mission.*
*UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.*
*UnitedHealth Group is a drug\-free workplace. Candidates are required to pass a drug test before beginning employment.*
Salary Context
This $386K-$579K range is above the 75th percentile 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 UnitedHealthcare, 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($483K) sits 125% above the category median. Disclosed range: $386K to $579K.
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.
UnitedHealthcare AI Hiring
UnitedHealthcare has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Cypress, CA, US, Minnetonka, MN, US. Compensation range: $163K - $579K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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