Interested in this AI/ML Engineer role at Optum?
Apply Now →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.
The Sr. Deployment Manager, AI & Product Office gets the office's AI-enabled products into use and keeps them improving, helping the Chief AI & Product Officer (CAIPO) drive Optum Health's enterprise AI-enabled product strategy, Agenix solutions portfolio, and product capability agenda. This role runs deployment into local teams, trains the people who use each product, and runs the cycle that turns what users see after launch into fixes that are measured.
This individual is accountable for adoption and measurable enterprise value: whether each product is used, whether it keeps getting better, and whether the next market can go live faster than the last. An AI product is not finished at launch: agents drift, workflows change, and the people using it see the problems first.
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 use AI tools to generate training, capture feedback at scale, and track adoption without a person chasing it.
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:
Deploy
- Plan and run each deployment into local teams: readiness, sequencing across sites, go-live support, and the decisions and Sring a go-live needs from market leaders
- Work with the team's engineers on releases, so what reaches users is ready for them and they know what changed
- Put the service design into practice with the service designer, so what was designed is what goes live
- Develop and execute change management and communications plans for each deployment, so the people affected understand the purpose, impact, readiness requirements, and expected value
- Partner with finance, technology, data, security, legal, privacy, compliance, care teams, and other stakeholders so each deployment is responsible, secure, and enterprise-ready
Iterate
- Run the improvement cycle after launch: capture what users see, triage it, get it to the right person on the team, and measure whether the fix worked
- Track adoption and the outcome measures for each product, and raise it early when a product is not being used or an agent's performance is drifting
- Keep the loop closed with the people using the product: they hear what changed because of what they reported
- Write down what the team learns each week so it is reviewed on cadence and turned into decisions
Train
- Design and run training for care teams, Sr, and managers on each product, built for how they work and generated with AI tools where that is faster
- Build a network of local champions in each market who can train others and bring feedback back
- Raise the AI fluency of the market teams you work with, helping local teams use and build with AI tools safely, on a supported path: templates, evaluation bars, and a way to ship
- Write the deployment and training playbook from the first market so the next market can go live from what is written down
- Determine which parts of a deployment belong to a single market and which belong to the enterprise, and write the playbook accordingly
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 deploying technology, or launching new sites, services, or markets, in healthcare delivery settings
- Experience in the healthcare industry and/or within a large, complex, matrixed organization
- Has led the deployment of a new technology or the launch of a new site or market, and can say what adoption and outcomes looked like after launch
- Has designed and delivered training for frontline clinical or operational staff
- Uses 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
- Able 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: tracks adoption and outcome measures and analyzes them with spreadsheets, SQL, or similar tools
- Strong 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, care team members, and market leaders
- Working proficiency in the tools this office builds with: GitHub and AI coding and agent tools
- Works as a senior individual contributor: owns hard problems end to end, working across functions to deliver them
Preferred Qualifications:
- Electronic health record implementation or training credentials, such as Epic
- Change management training or certification
- Experience deploying across multiple sites or markets and adapting to local differences
- Experience supporting an AI or machine learning product after launch
- 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
- Deployment planning and go-live execution
- Change management and stakeholder readiness
- Post-launch iteration and feedback loops
- Training design and delivery for frontline teams
- Adoption and outcome measurement
- 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 to $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 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $112K to $193K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Optum AI Hiring
Optum has 48 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Engineering Manager, AI Software Engineer. Positions span Minnetonka, MN, US, Boston, MA, US, Eden Prairie, MN, US. Compensation range: $163K - $348K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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