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About This Role
Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.
This senior enterprise product leadership role shapes, scales, and operationalizes the company's AI productivity tools portfolio, including Microsoft Copilot, ChatGPT Enterprise, and Claude Enterprise.
Operating at the intersection of enterprise transformation, product strategy, enablement, and value realization, this leader owns the end\-to\-end approach for how generative AI productivity tools are introduced, governed, adopted, measured, and continuously improved across the enterprise.
The role is accountable not just for tool availability, but for making AI productivity repeatable and valuable in day\-to\-day work\-moving the organization from early experimentation to sustained, measurable impact at scale. Success is defined by employees selecting the right tools for their work, understanding core capabilities, and applying them to deliver measurable productivity and business value.
The ideal candidate is a decisive, enterprise\-minded product leader with strong executive presence, deep curiosity, and a bias for action. They are comfortable operating in ambiguity, influencing without authority, and owning outcomes in a fast\-moving, highly visible space.
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:
- Portfolio Strategy \& Product Leadership
+ Own the enterprise AI productivity tools portfolio strategy (Microsoft Copilot, ChatGPT Enterprise, Claude Enterprise), including positioning, differentiation, and recommended usage patterns by role and function
+ Define the long\-term vision, near\-term roadmap, and success measures for AI productivity tools as part of the broader employee experience and AI transformation agenda
+ Balance innovation and experimentation with enterprise requirements for security, compliance, cost transparency, and responsible AI use
+ Lead and develop a global product team (product and enablement owners), building a high\-performing, AI\-fluent organization focused on measurable adoption and business value
+ Define and manage value\-realization measures and communicate outcomes through concise, executive\-ready storytelling
+ Partner with executive leadership to align AI productivity tools to enterprise priorities, transformation goals, and workforce impact
+ Proactively identify gaps, opportunities, and risks, and drive decisions that move from strategy to execution
+ Stay current on the AI landscape\-tracking vendor announcements, emerging capabilities, and industry trends to inform strategy, roadmap decisions, and adoption plans
- Enablement, Adoption \& Evangelization
+ Shape the workforce AI skills strategy\-ensuring employees not only adopt tools, but build lasting capability and confidence in applying AI to real work
+ Design and lead a multi\-layered enablement strategy that includes self\-service learning, targeted training, hands\-on experiences, communities of practice, and in\-the\-flow\-of\-work guidance
+ Partner with stakeholders and executives to evangelize AI productivity tools through practical, role\-based stories and examples
+ Ensure enablement is outcome\-oriented\-moving beyond tool training to sustained behavior change and productivity gains
- Operationalization \& Governance
+ Own prioritization and decision\-making for the AI productivity tools portfolio, balancing innovation, risk, cost, and value realization
+ Partner with product operations to define and maintain the operating model for AI productivity tools, including intake, access models, support patterns, escalation paths, and lifecycle management
+ Coordinate across platform, security, finance, legal, and support teams to ensure smooth onboarding, clear guardrails, and scalable operations
+ Use real\-world feedback to continuously refine governance and reduce friction while maintaining enterprise standards
- Measurement, Insights \& Value Realization
+ Establish clear adoption, usage, and value metrics for AI productivity tools aligned to enterprise transformation goals
+ Synthesize qualitative and quantitative insights to inform roadmap decisions, executive updates, and investment prioritization
+ Translate signals from support, training, and usage data into actionable improvements across products, enablement, and policy
- Cross\-Functional Leadership \& Influence
+ Act as a connector across product, engineering, platform, enablement, and business stakeholders, enabling shared accountability for AI productivity outcomes
+ Influence senior leaders by framing AI productivity in terms of measurable business outcomes, not tools or features
+ Own strategic vendor relationships across the AI productivity portfolio, influencing roadmaps, feature prioritization, and enterprise readiness so tools evolve in line with organizational needs and value objectives
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:
- 3\+ years of experience in enterprise product management, platform leadership, or digital transformation
- 1\+ years of experience delivering AI\-powered or user\-facing technology products (e.g., applied AI, intelligent agents, IT automation) in production environments
- Demonstrated experience leading enterprise\-scale productivity or enablement platforms, preferably spanning multiple tools or vendors
- Vendor management experience, including licensing, roadmap engagement, and operational readiness
- Solid understanding of generative AI concepts, productivity patterns, and responsible AI considerations
- Solid understanding of enterprise technology ecosystems, including cloud platforms, APIs, architecture patterns, and modern digital experience platforms
- Proven ability to operate across strategy, execution, and influence, including senior executive engagement
- Proven high AI acumen across the enterprise productivity tool suite, with a current understanding of capabilities, limitations, and roadmaps to guide strategy, adoption, and value realization
- Demonstrated fluency using enterprise\-approved AI tools in daily product management work to drive insight, efficiency, and decision\-making
- Proven excellent stakeholder management skills, with the ability to align business, technology, and delivery teams around a shared vision and influence without authority
- Proven solid analytical and decision\-making skills, with a focus on metrics, outcomes, and continuous improvement
Preferred Qualifications:
- Experience launching or scaling Microsoft Copilot, ChatGPT Enterprise, Claude, or similar AI tools in a large enterprise
- Experience working in highly regulated or complex enterprise environments
- Familiarity with enterprise financial models (chargeback, cost attribution, licensing strategies)
- Proven background in change management, enablement, training, or workforce transformation
- Proven ability to build, lead, and mature global product teams
- Proven solid executive presence, with a track record of partnering with senior leaders to align priorities, manage risk, and drive measurable outcomes
- Proven financial acumen, including building cost\-benefit analyses and business cases to support product decisions
- 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 $134,600 \- $230,800 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 $134K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($182K) sits 16% below the category median. Disclosed range: $134K to $230K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Optum AI Hiring
Optum has 19 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager, Research Scientist. Positions span Eden Prairie, MN, US, Minnetonka, MN, US, San Francisco, CA, US. Compensation range: $134K - $302K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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