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About This Role
Optum Tech is a global leader in health care innovation. Our teams develop cutting\-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.
The Vice President of AI/ML Engineering for Medicare \& Retirement (M\&R) is responsible for driving AI\-first engineering transformation across the portfolio, enabling scalable, production\-grade AI capabilities that power growth, cost optimization, and operational efficiency for Medicare Advantage and Dual Eligible Special Needs Plan (DSNP) businesses.
This leader translates complex healthcare business challenges\-such as benefit design, clinical operations, member engagement, and sales optimization\-into deployable AI solutions, platforms, and products. The role will lead enterprise AI/ML engineering at scale, embedding AI across plan design, digital experiences, and operational workflows.
The impact of this role is direct and measurable: driving revenue growth and retention, increasing market responsiveness, strengthening resiliency and compliance, and reducing cost to serve through AI first modernization, platform reuse, and operational excellence\-while modeling UnitedHealth Group values of Integrity, Quality, Inclusion, Compassion, Relationships, Innovation, and Performance.
You'll enjoy the flexibility to work remotely \* from anywhere within the U.S. as you take on some tough challenges.
For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.
Primary Responsibilities:
AI Strategy \& Portfolio Transformation
- Define and execute an enterprise AI/ML strategy aligned to M\&R business priorities and growth objectives
- Embed AI across core value streams: benefit design, member lifecycle, and clinical operations
- Drive transition from experimentation to scaled, production\-grade AI across the portfolio
AI/ML Platform \& Engineering Leadership
- Build and scale enterprise AI/ML platforms supporting model development, deployment, and monitoring
- Enable real\-time decisioning and batch analytics at scale
- Standardize MLOps, AI pipelines, and engineering delivery practices
- Drive reusable AI services and shared capabilities across the enterprise
- Accelerate adoption of GenAI, LLMs, and agent\-based systems where appropriate
Business Alignment \& Value Delivery
- Partner with Product, Business (M\&R leadership), and Finance to prioritize high\-value AI use cases
- Align AI investments to measurable ROI outcomes across growth, clinical, and operational domains
- Translate business problems (pricing, engagement, operations) into deployable AI solutions
- Scale pilots into enterprise\-grade platforms
Engineering Execution \& Operational Excellence
- Lead delivery of production\-grade AI systems with high reliability, scalability, and compliance
- Establish best practices for model validation, testing, and monitoring
- Optimize performance and cost efficiency across AI workloads and infrastructure
- Embed AI into SDLC and operational workflows as a standard engineering capability
AI, Automation \& Healthcare Impact (M\&R\-Specific Outcomes)
- Lead AI/ML engineering across the M\&R portfolio, enabling:
+ Benefit design optimization
+ Ancillary benefit optimization and validation
+ DSNP and food benefit innovation
+ Clinical and Medicare STARS automation
- Deliver measurable outcomes including:
+ Revenue growth and marketing effectiveness
+ Medical cost optimization and risk adjustment
+ Cost\-to\-serve reduction through automation
+ Improved Medicare STARS quality performance
Responsible AI, Risk \& Compliance
- Ensure AI solutions comply with healthcare regulations and data privacy standards
- Establish responsible AI practices including fairness, explainability, and auditability
- Partner with security and risk teams to govern models and manage lifecycle risk
AI Transformation Leadership
- Lead the AI Blueprint transformation for M\&R Technology, converting enterprise AI priorities into a clear roadmap, governance model, and execution plan
- Align AI priorities and investments to measurable business value
- Drive scaled adoption of AI\-first engineering practices, including AIDLC, reusable AI capabilities, responsible AI controls, and enterprise tooling
- Advance the M\&R AI talent and workforce strategy through role\-based enablement, adoption metrics, and operating model changes that build sustained AI capability
- Monitor transformation progress, risks, dependencies, and outcomes through executive reporting tied to value, adoption, delivery, and responsible AI performance
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:
- 15\+ years of software engineering experience, with 5\+ years in senior engineering leadership roles
- Proven track record modernizing large scale legacy platforms and establishing AI enabled, cloud native architectures
- Employ AI and/or ML that may include natural language processing (NLP), natural language understanding (NLU), semantic understanding, intent classification, computer vision, deep learning, and automatic speech recognition (ASR)
- Work with large scale computing frameworks and data analysis systems. Employees in this role are expected to apply knowledge of experimental methodologies, statistics, optimization, probability theory and machine learning using code for tool building, statistical analysis, using both general purpose software and statistical languages
- Demonstrated success leading matrixed, globally distributed engineering teams
- Deep expertise in cloud platforms (AWS, Azure, GCP), microservices, APIs, DevOps, application security, and modern SDLC and AI\-SDLC practices
- Hands on leadership integrating AI/ML and GenAI capabilities into production platforms and developer workflows
- Exceptional communication, influencing, and executive stakeholder management skills
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field
Preferred Qualifications:
- Product oriented engineering leader with strong focus on platform reuse, developer experience, and AI enabled productivity
- Experience leading AI first transformation initiatives, including cloud migration, platform consolidation, and intelligent automation
- Proven experience deploying AI/ML Models at scale across Payers / Providers / Healthcare ecosystem OR similarly regulated industries
- Knowledge of Quantum computing
- 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 $200,400 to $343,500 annually based on full\-time employment. We comply with all minimum wage laws as applicable.
*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 $200K-$343K 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 Optum, 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. This role's midpoint ($271K) sits 27% above the category median. Disclosed range: $200K to $343K.
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.
Optum AI Hiring
Optum has 18 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Minnetonka, MN, US, Eden Prairie, MN, US, Brentwood, TN, US. Compensation range: $176K - $348K.
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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