Interested in this AI/ML Engineer role at Optum?
Apply Now →About This Role
Optum Insight is improving the flow of health data and information to create a more connected system. We remove friction and drive alignment between care providers and payers, and ultimately consumers. Our deep expertise in the industry and innovative technology empower us to help organizations reduce costs while improving risk management, quality and revenue growth. Ready to help us deliver results that improve lives? Join us to start Caring. Connecting. Growing together.
The Director, Provider AI Innovation and Strategy leads the end-to-end delivery of AI transformation engagements and AI-enabled offerings serving health systems, hospitals, physician groups, ambulatory organizations, post-acute providers, and other care-delivery organizations. This leader serves as the primary engagement lead and senior client advisor, aligning business priorities, stakeholders, and cross-functional delivery teams to translate strategic objectives into executable AI initiatives. The role is accountable for shaping transformation roadmaps, guiding solution development and implementation, managing delivery outcomes, and identifying opportunities to expand client impact through scalable AI-enabled capabilities.
The Director will build executive relationships, shape new opportunities, support sales pursuits, and convert emerging AI capabilities into differentiated offerings. The role has a primary focus on growth, commercialization, and strategic client relationships, while maintaining active involvement in critical delivery activities to ensure solutions improve clinical, operational, financial, and patient outcomes.
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:
- Engagement & Delivery Leadership: Lead complex AI transformation engagements from strategy through implementation and value realization, ensuring alignment across client executives, business stakeholders, and cross-functional delivery teams
- Executive Client Leadership: Serve as a senior advisor to client executives throughout delivery, facilitating critical decisions, resolving escalations, aligning stakeholders, and guiding changes to operating models, investment priorities, adoption, governance, and value realization
- Cross-Functional Delivery Leadership: Mobilize and lead multidisciplinary teams across strategy, product, engineering, data science, operations, legal, compliance, and other functions to deliver integrated AI solutions in complex environments
- Client Growth: Build and expand relationships with health system executives, clinical leaders, operational leaders, financial leaders, and technology leaders
- Opportunity Development: Identify, qualify, and shape strategic opportunities involving clinical care, patient access, care management, revenue cycle, workforce, quality, capacity, consumer experience, population health, and administrative operations
- Sales and Pursuit Leadership: Lead or support proposals, executive presentations, demonstrations, workshops, pricing discussions, and complex sales pursuits
- Provider Thought Leadership: Develop pioneering approaches to emerging provider-industry trends and communicate the implications of AI for care delivery, clinician workflows, patient experience, health system operations, and financial performance
- Executive Advisory: Advise senior provider executives on AI strategy, investment priorities, governance, operating-model changes, workforce adoption, and value realization
- Data and Workflow Integration: Shape solutions that use clinical, claims, operational, financial, consumer, scheduling, and workforce data and integrate appropriately with provider technology and workflow environments
- Innovation Intake: Evaluate research outputs, emerging AI capabilities, internal assets, and third-party technologies for applicability to provider needs
- Pricing and Business Planning: Develop business cases, pricing approaches, revenue forecasts, resource plans, and performance measures in partnership with sales, operations, and finance
- Partnerships and Ecosystem: Develop relationships with technology vendors, data partners, academic organizations, and other healthcare ecosystem participants
- Risk and Responsible AI: Incorporate patient safety, clinical governance, privacy, security, interoperability, model risk, workflow impact, and responsible AI considerations into solution decisions
- Performance Management: Track pipeline, sales, revenue, profitability, adoption, clinical or operational impact, and client satisfaction
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 Business, Healthcare Administration, Engineering, Computer Science, or a related field; Master's degree (MBA, MHA, MPH, or equivalent) preferred
- 10+ years of progressive experience in strategy, management consulting, product management, commercialization, business development, healthcare operations, or technology-enabled solution delivery
- 5+ years of experience with a top-tier management consulting firm (e.g., McKinsey, Bain, BCG, Deloitte, Accenture Strategy, or equivalent), including responsibility for leading client engagements and delivering executive-level recommendations
- 5+ years of experience partnering with healthcare provider organizations, including health systems, hospitals, physician groups, ambulatory care organizations, post-acute providers, or integrated delivery networks
- 5+ years of experience developing and managing executive-level client relationships, including identifying, influencing, and closing complex consulting, technology, analytics, or AI-enabled opportunities
- 5+ years of experience leading large-scale, cross-functional programs involving multiple business units, stakeholders, and competing priorities
- Experience leading the development and commercialization of new products, services, or technology-enabled solutions from concept through implementation and market adoption
- Experience developing business cases, pricing strategies, revenue forecasts, go-to-market plans, and value propositions supporting growth initiatives exceeding $5M in annual revenue opportunity
- Experience delivering executive presentations, facilitating strategic decision-making discussions, and negotiating outcomes with senior leaders and external clients
- Experience supporting provider transformation, operational improvement, digital transformation, or technology implementation programs within complex healthcare environments
- Demonstrated expertise in at least one provider-focused functional area, including clinical operations, patient access, revenue cycle management, care management, quality improvement, workforce optimization, capacity management, consumer experience, or population health
- Demonstrated knowledge of artificial intelligence, machine learning, cloud technologies, healthcare data ecosystems, interoperability standards, and product development best practices
- Proven ability to influence VP-level and above stakeholders and drive adoption of new business strategies, products, technologies, or operating models
- Willing and able to travel up to 30% to 40% as required
Preferred Qualifications:
- 12+ years of combined consulting and healthcare industry experience
- 3+ years of experience developing or deploying AI-enabled, analytics, or digital transformation solutions within provider organizations
- Experience leading initiatives that generated measurable financial, operational, clinical, or customer experience improvements
- Experience working with C-suite executives of health systems, provider organizations, or healthcare technology companies
- Experience contributing to annual revenue growth, client expansion, or new business development efforts exceeding $10M annually
- 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 $134,600 - $230,800 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 $134K-$230K range is above the median 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 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($182K) sits 14% below the category median. Disclosed range: $134K to $230K.
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.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 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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