Interested in this AI/ML Engineer role at Optum?
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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 in making healthcare work better for everyone through people-led, responsible AI while Caring. Connecting. Growing together.
Position Summary
The Service Designer, AI & Product Office designs how care and operations change when an AI-enabled product arrives, helping the Chief AI & Product Officer (CAIPO) drive Optum Health's enterprise AI-enabled product strategy, Agenix solutions portfolio, and product capability agenda. Serving as observer, designer, and operator, this role maps how care runs today and designs the future state with the people who do the work: the roles, workflows, handoffs, and operating decisions around the software.
This individual is accountable for measurable enterprise value in the operation: whether the product and the operation around it were designed together, and whether the change shows up in how care is delivered. The Service Designer turns what one market learns into a service design the next market can adapt.
This is a job for a builder. The team is just starting, and you will help build it and shape how it innovates, at the highest level. The office is small today, and that is by design: its leverage is the enterprise AI investment it drives across Optum Health. You will spend real time where care is delivered, with the care teams who deliver it, and you will use AI tools to take the manual work out of observing, mapping, and synthesizing.
The AI & Product Office is building a small, cross-functional product team: product management, engineering, product design, service design, deployment, and clinical, working alongside care team members from each local market team. The team embeds with a local Optum Health market team to find problems with the people who do the work, then design, build, deploy, and measure the products that fix them. What the team learns in the first market is written down as the method for the next one.
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:
- Map & Discover + Embed with local teams to shadow and map how care is delivered today, end to end, across primary care, specialty, home and community, and virtual care, including the handoffs, systems, and points where patients drop out + Plan and facilitate kaizen events and working sessions with frontline staff, care team members, and managers to find the problems worth fixing and design the future-state workflow with them + Write down what you observe each week so it is reviewed on cadence, turned into decisions, and usable by the team and the next market's team without you in the room
- Design the Service + Design the service blueprint around each product: roles, workflows, scripts, handoffs, staffing, and the policies that change when the product arrives + Partner with product, design, and engineering to decide what the product should do and what the operation should do + Partner with market operations leaders, practice managers, and clinical leaders on the operating decisions a new service needs, and determine which decisions belong to a single market and which belong to the enterprise + Partner with finance, technology, data, security, legal, privacy, compliance, care teams, and other stakeholders so each service design is responsible, secure, and enterprise-ready
- Measure, Adopt & Hand Off + Define the operational measures, such as access, time to care, and staff time, and establish baselines before launch so the change can be measured + Develop change management plans with the deployment manager, so the people affected understand the purpose, impact, readiness requirements, and expected value of each change + Hand the service design to the deployment manager for go-live, and stay with it through launch so what was designed is what goes live + Write the service design down so the next market can adapt it to its own operation rather than start again + Raise the AI fluency of the market teams you work with, helping the people closest to the work use and build with AI safely
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:
- 7+ years of experience in operations, process improvement, or service design, with significant time in care delivery settings
- Experience in the healthcare industry and/or within a large, complex, matrixed organization
- Experienced to design a new service or operating model with clinical or operational teams, seen it through launch, and can say what changed as a result
- Experience planning and facilitating Lean, kaizen, or similar improvement events with frontline teams
- Experience using AI tools in your own work today and has built with them: an agent, automation, or large language model tool you designed, built, and ran, at work or as a personal project We will ask you about it in the interview
- Works as a senior individual contributor: owns hard problems end to end, working across functions to deliver them
- Working proficiency in the tools this office builds with: GitHub and AI coding and agent tools, alongside process mapping and analytics tools
- Demonstrated ability to work at the enterprise level and at a single market's level, and to tell which one a given decision belongs to
- Demonstrated business acumen: defines operational measures, builds a baseline, and analyzes results with spreadsheets, SQL, or similar tools
- Proven solid writing: decisions, findings, and disagreements in this office are written down, so the writing is the work
- Proven ability to build relationships at all levels of the organization, earning trust with frontline staff and care team members as well as with operations leaders
Preferred Qualifications:
- Bachelor's degree in a related field; advanced degree welcome
- Training in service design or human-centered design methods
- Lean or Six Sigma certification
- Experience in care delivery, care coordination, patient access, scheduling, or practice operations
- Experience designing services across multiple sites or markets and adapting to local differences
- Clinical or practice background, such as nursing, medical assistant, or practice management
Core Competencies:
- Building with AI: uses agents and automation to do the job, not only to describe it
- Current-state mapping and field observation
- Service and workflow design
- Facilitation of improvement events with frontline teams
- Operating model and policy design
- Operational measurement
- Change management and stakeholder readiness
- Enterprise and market-level judgment
- Relationship building across care teams and operations
- 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 $112,700 - $193,200 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 help make the health system work better for everyone. Together, we are shaping the future of healthcare by harnessing technology and innovation to make care simpler to navigate, more affordable and more connected for the people we serve. We are committed to creating an inclusive workplace where everyone feels welcomed, valued, heard and respected, empowering people to bring their authentic selves to work and strengthening our collective impact through diverse talents, backgrounds, experiences and perspectives.
UnitedHealth Group and its affiliated brands are 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 and its affiliated brands are 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: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $112K to $193K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Optum AI Hiring
Optum has 38 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span Boston, MA, US, Minnetonka, MN, Dublin, County Dublin, Ireland. Compensation range: $163K - $343K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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