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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.
Our mission is to make data trustworthy and actionable so teams can deliver smarter consumer experiences. We are evolving the Experience Data Platform to unify fragmented data and enable AI-ready, real-time activation that simplifies and personalizes healthcare. This role plays a key part in delivering capabilities partners can adopt quickly and operate with confidence. You will thrive here if you enjoy complex problems, cross-functional collaboration, and visible impact.
Reporting to the Director, Data Strategy & Stewardship, the Sr. Digital Product Manager is an individual contributor who turns business needs and data strategy into shippable capabilities on the Experience Data Platform. Embedded with an assigned engineering pod, they lead discovery, define features and epics for one or more data product areas, and partner with the Lead Digital Product Manager to translate business needs into clear, prioritized work.
The Sr. Digital Product Manager manages the product backlog and end-to-end delivery for their assigned epics and features with their engineering pod. Working in an Agile Scrum environment, they lead key ceremonies, coordinate with Engineering and other delivery teams, and ensure high-quality, on-time releases that meet business, technical, and compliance standards.
Success in this role requires strong product management skills, experience with agile engineering teams, and the ability to collaborate with both technical and non-technical partners. The Sr. Digital Product Manager balances trade-offs between scope, timing, and quality, contributes to roadmap planning, and maintains transparency and alignment across stakeholders.
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:
- Write and refine epics, features, user stories, and acceptance criteria that guide the assigned engineering pod and reflect data quality, compliance, and performance requirements
- Partner with the Lead Digital Product Manager to translate strategy and roadmap into clear, prioritized work for the pod and related delivery teams
- Serve as product owner for an assigned engineering pod, managing the product backlog and sprint planning to ensure aligned, predictable delivery
- Support creation and refinement of product roadmaps by providing delivery insights, risks, and recommendations from the pod's work
- Lead and participate in agile ceremonies (e.g., backlog refinement, sprint planning, daily stand-ups, demos, and retrospectives) with the pod, ensuring transparency across progress, risks, and decisions
- Drive delivery across data domains such as ingestion, publishing, and transformation with the pod, coordinating dependencies with other Engineering and Architecture teams
- Ensure alignment between business needs and technical execution throughout the product lifecycle by being the day-to-day product contact for the pod
- Track and actively optimize key delivery milestones, dependencies, blockers, and risks for the pod to maintain visibility into capability roadmaps and upcoming releases
- Incorporate feedback from stakeholders, end users, and experiments (e.g., pilots, A/B tests) into backlog refinement and future sprint planning
- Analyze delivery and product usage metrics; prepare simple reports or dashboards and recommend improvements to flow, quality, and customer or stakeholder experience
- Contribute to the continuous improvement of agile practices and product management processes within the Data Strategy & Stewardship team
- Serve as a resource to junior team members (where applicable) by sharing best practices and providing informal guidance when needed
- Perform other duties as assigned
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:
- Demonstrated experience serving as Product Owner for an embedded engineering pod (or equivalent), driving prioritization and managing delivery trade-offs
- Demonstrated experience translating business needs and user insights into epics, user stories, and clear acceptance criteria
- Demonstrated experience partnering across Architecture, Engineering, and business stakeholders to deliver platform capabilities
- Solid written and verbal communication skills, including the ability to drive alignment across technical and non-technical teams and provide executive-ready updates
- Demonstrated ability to navigate ambiguity and shifting priorities while maintaining delivery focus and accountability
- Solid analytical and problem-solving skills, using data and metrics to assess performance and inform decisions
- Demonstrated ability to build trust and collaborate effectively across teams and stakeholders
Preferred Qualifications:
- Hands-on experience with modern cloud data and analytics platforms (e.g., Azure, Databricks, Snowflake, Power BI/Tableau, Adobe Experience Platform)
- Experience supporting enterprise-scale consumer data platforms and data domains (e.g., ingestion, transformation, publishing)
- Experience analyzing and synthesizing data to inform roadmap priorities, identify risks/opportunities, and recommend actions
- Experience using product and delivery tooling (e.g., Aha!, Rally, or similar) to manage work, dependencies, and delivery reporting
- Familiarity leveraging AI-enabled tools to accelerate analysis, reporting, and/or product workflow (e.g., automated insights, anomaly detection, AI-assisted documentation)
- Experience working with AI-enabled, agentic, or automation-driven products, including translating business problems into use cases, requirements, guardrails, success measures, and prioritized product capabilities
- Familiarity with AI development lifecycle concepts and PRD generation, including rapid experimentation, prototyping, evaluation, testing, deployment, performance monitoring, and continuous optimization of AI-enabled capabilities
- 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 $91,700 to $163,700 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 $91K-$163K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($127K) sits 40% below the category median. Disclosed range: $91K to $163K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Optum AI Hiring
Optum has 31 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Engineer. Positions span Schaumburg, IL, US, San Francisco, CA, US, Charleston, WV, US. Compensation range: $74K - $302K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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