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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 accelerating its next\-generation AI transformation through Claims AI 10\.0 and Enterprise Agentic AI initiatives. We are seeking a highly experienced Senior Manager AI/ML Engineering to lead multiple AI engineering teams responsible for building and scaling enterprise\-grade AI, Generative AI, Agentic AI, and intelligent automation solutions.
This leader will oversee delivery of strategic AI platforms and business\-critical AI products supporting claims automation, intelligent decision systems, knowledge\-driven workflows, and healthcare operational transformation. The role requires a strong blend of technical leadership, organizational leadership, people management, platform strategy, delivery excellence, and enterprise AI governance.
The ideal candidate will have experience leading large\-scale AI engineering organizations, driving AI platform adoption, managing complex cross\-functional programs, and delivering measurable business outcomes using AI technologies.
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:
- Lead AI/ML adoption across the team, driving innovative use of both Generative AI and traditional ML techniques
- Design, develop, test, deploy, maintain, and continuously improve AI\-powered software solutions to meet customer and business needs
- Build responsible AI solutions and service components to support the Provider community, ensuring compliance with ethical and regulatory standards
- Leverage emerging offerings from cloud service providers to enable efficient and cost effective AI solutions
- Develop new features leveraging AI/ML, including Generative AI models, predictive analytics, and automation capabilities
- Provide technical leadership for AI initiatives, including Chatbots and conversational AI solutions
- Partner with product management, architects, and engineering leadership to deliver creative, scalable AI solutions for healthcare challenges
- Drive alignment with enterprise AI strategies and ensure solutions meet business objectives and engineering best practices
- Accountable for high\-quality software deliverables and operational excellence
- Mentor and train team members on AI/ML technologies, frameworks, and responsible AI practices
- Collaborate with broader engineering teams to integrate AI into cloud modernization efforts and digital\-first initiatives
- Exercise independent judgment to prioritize, plan, and manage AI\-related activities and projects
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 or Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or related technical field
- 12\+ years of software engineering experience
- 8\+ years of AI/ML engineering experience
- 5\+ years of experience managing engineering teams, managers, and technical leaders
- 5\+ years of experience delivering enterprise\-scale AI, ML, Data, or Cloud platforms
- 4\+ years of hands\-on experience with Generative AI, LLMs, NLP, and AI architecture
- 3\+ years of experience with Agentic AI, RAG, AI orchestration, and intelligent automation
- Experience with AWS cloud\-native services including Bedrock, SageMaker, Lambda, ECS/EKS, OpenSearch, and S3
- Experience leading AI governance, model lifecycle management, operationalization, and production support processes
- Solid Python development background and familiarity with enterprise\-scale AI platforms
Preferred Qualifications:
- Experience managing multi\-million\-dollar AI transformation programs
- Exposure to healthcare payer, claims, provider operations, utilization management, or regulated industries
- Experience driving enterprise cloud modernization and platform transformation initiatives
- Solid executive communication, strategic planning, stakeholder management, and organizational leadership skills
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- 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 $148,900 \- $255,300 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.*
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At Optum, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $249,650 based on 10 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($202K) sits 19% below the category median. Disclosed range: $148K to $255K.
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 Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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