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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.
We are seeking a Lead AI Technical Product Manager to join our Optum Tech UHC Technology team within the UHC Operations and Experience division. In this role, you will own AI product strategy end\-to\-end \- defining the vision, roadmap, and long\-term strategy while aligning technology delivery with business priorities. Acting as the critical bridge between business outcomes, data readiness, model capabilities, and engineering execution, you will move beyond research and prototyping to deliver and maintain applied AI systems in production. You will design and scale modern, high\-quality technical platforms that leverage data, automation, and AI/ML \- including generative AI (GenAI), agentic AI, and autonomous AI agents \- to improve operational efficiency and business outcomes. Partnering closely with engineering, data science, and design teams, you will champion Agile execution, full model lifecycle ownership, and the responsible use of AI to build scalable, secure systems that drive measurable business impact.
Primary Responsibilities:
- Product Strategy \& Vision: Own the end\-to\-end AI product strategy \- vision, roadmap, prioritization, and long\-term strategy aligned with business priorities; translate business goals into clear product objectives, success metrics, and quantifiable KPIs
- AI \& Solution Design: Design, develop, and deploy AI\-powered, automation\-driven, and data platform solutions to complex business challenges, translating AI/ML capabilities into clear value propositions for users and business leaders
- Generative \& Agentic AI: Drive the adoption and productization of generative AI (GenAI), agentic AI, and AI agent solutions \- including LLM\-powered applications, multi\-agent orchestration, retrieval\-augmented generation (RAG), and autonomous or semi\-autonomous workflows \- while defining the guardrails, evaluation criteria, and human\-in\-the\-loop controls needed for safe, reliable operation
- Applied AI \& Model Lifecycle Ownership: Be accountable for the performance and reliability of AI systems in production \- moving beyond research and prototypes to scalable, real\-world applications \- and manage the complete model lifecycle so solutions are continuously monitored, evaluated, and improved based on production feedback
- Workflow \& Process Optimization: Leverage and advocate for enterprise\-approved AI tools and modern technologies (cloud, automation, AI/ML) to streamline workflows, automate tasks, and drive continuous improvement across operations
- Delivery \& Agile Execution: Own the end\-to\-end product lifecycle from ideation through development, launch, and optimization. Prioritize the product backlog, define features, write specifications, and drive Agile ceremonies (planning, grooming, reviews)
- Cross\-Functional Partnership: Lead cross\-functional teams across engineering, data science, design, and business stakeholders to deliver high\-quality, scalable, and secure solutions, evaluating technical architectures and cloud approaches
- Stakeholder Management: Act as the primary interface between business stakeholders, leadership, and technical teams; communicate product strategy, progress, risks, and outcomes clearly to executive leaders
- Data \& Platform Enablement: Lead the development of data platforms, reporting solutions, and integrated data pipelines while defining data requirements, governance standards, and integration approaches to ensure data readiness for AI
- Responsible AI \& Risk Governance: Champion the ethical use of AI by embedding transparency, fairness, and accountability throughout the AI lifecycle; identify and manage risks \- including incorrect predictions, unintended bias, and downstream business impact \- while ensuring strict compliance with enterprise data privacy and security policies
- Customer \& Business Impact: Gather and synthesize user feedback to refine product offerings, tracking performance metrics to ensure solutions drive measurable business value (efficiency, cost savings, and quality improvement)
- AI Tool Proficiency \& Adoption (AIDCL)
+ These resources are expected to demonstrate baseline proficiency in enterprise\-approved AI tools as part of their day\-to\-day responsibilities, This includes, but is not limited to:
+ Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise
+ Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision\-making workflows
+ Continuous Learning: Stay current with evolving AI capabilities and features, and apply them to improve delivery quality and velocity
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:
- Bachelor's degree in Engineering, Computer Science, Business, or a related field, OR equivalent experience (4\+ years of additional product management or technology delivery experience in lieu of a degree)
- 10\+ years of experience in technical product management, technology delivery, or data platform management
- 5\+ years of experience leading complex, enterprise\-scale technology products from concept to implementation (vision to production)
- 3\+ years of experience writing business requirements and technical product specifications within an Agile development methodology
- 2\+ years of experience working directly with cloud technologies (e.g., Snowflake, Azure, AWS) and data/analytics architectures
- 2\+ years of experience delivering or managing AI\-enabled or Applied AI products in production (real\-world delivery, not just research or prototyping)
Preferred Qualifications:
- Master's degree in Engineering, Computer Science, Business, or a related field
- 3\+ years of experience delivering and managing Applied AI or Production AI systems, including generative AI or agentic AI solutions, in real\-world environments
- Hands\-on experience delivering generative AI or agentic AI solutions in production (e.g., LLM applications, AI agents, multi\-agent systems, RAG pipelines, or agent orchestration frameworks)
- Experience defining and driving product roadmaps and strategic visions for data\-driven, automated, or AI/ML\-powered systems
- Experience with model lifecycle ownership, including monitoring and performance tuning in production environments
- Experience managing data governance, privacy compliance, and security policies within an enterprise environment
- Working fluency in AI/ML concepts \- including an understanding of model capabilities, limitations, outcomes, and data requirements (non\-hands\-on modeling) \- with a working understanding of generative AI and agentic AI concepts such as large language models (LLMs), AI agents, prompt engineering, and orchestration/RAG patterns
- Technical working knowledge of APIs, cloud platforms (primarily Azure), and the Software Development Life Cycle (SDLC)
- Proven ability to influence senior stakeholders and align cross\-functional, geographically distributed teams within a complex, matrixed global organization
- Proven excellent problem\-solving, risk mitigation, and decision\-making skills with a high ownership mindset
- Proven solid communication skills with a proven ability to translate complex technical and AI concepts into business\-friendly language for executive audiences
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 $112,700 \- $193,200 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 $112K-$193K range is below 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($152K) sits 30% below the category median. Disclosed range: $112K to $193K.
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.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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.
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