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About This Role
Optum Tech is a global leader in health care innovation. Our teams develop cutting\-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.
We are hiring a Director, FinOps \& AI Cost Engineering to build that system.
This is not a traditional cloud cost management role. It is a leadership role at the intersection of engineering, architecture, finance, procurement, and AI platform operations. The person in this seat will establish the frameworks, telemetry, governance, and operating rhythms that turn technology spend into measurable business value. This leader will help UnitedHealthcare Technology understand not just what AI costs, but which AI investments create the most value, how to scale them responsibly, and how to make those economics visible to engineering leaders and executives alike.
This role will be embedded within UHC Technology's platform and engineering ecosystem, partnering closely with SRE, observability, and engineering teams to drive cost visibility and optimization.
The right person for this role combines solid FinOps and cloud economics expertise with deep technical fluency in AI workloads, modern platforms, and engineering delivery. This leader must be equally comfortable discussing token\-level unit economics with engineers, commitment strategies with cloud partners, and value realization with senior leadership.
What You Will Own:
AI FinOps Strategy and Operating Model:
- Stand up and lead an AI FinOps practice spanning cloud infrastructure, on\-prem infrastructure, SaaS, vendor spend, model API usage, and token\-based AI workload economics
- Build and scale the FinOps capability from the ground up, establishing foundational practices, tooling, and operating rhythms where they do not exist today
- Define the operating model, governance, and decision rights for AI and tech cost accountability across products, platforms, and shared services
- Establish the standards for how AI spend is measured, attributed, reviewed, and optimized across UHC Technology
Unit Economics and Cost Transparency:
- Build the core economics framework for AI\-enabled technology services, including cost per inference, cost per agent run, cost per workflow, cost per active user, and cost per business outcome where practical
- Create showback and chargeback models that give engineering and business leaders actionable cost visibility at the team, application, and workload level
- Develop dashboards, scorecards, and reporting that make cloud and AI economics understandable to both technical and executive audiences
Optimization and Value Engineering:
- Identify and drive optimization opportunities across cloud consumption, commitments, rightsizing, storage, SaaS usage, model selection, prompt design, retrieval strategies, and inference patterns
- Partner with engineering and architecture leaders early in design decisions to shape solutions for both technical performance and economic efficiency
- Partner with SRE and observability teams to connect performance, reliability, and cost signals, enabling more informed engineering and architectural decisions
- Shift the organization from reactive bill review to proactive value engineering
- Enable scalable adoption of AI and platform capabilities by embedding cost transparency and economic efficiency into engineering decisions early in the lifecycle
AI\-Enabled FinOps Automation:
- Deploy AI\-powered workflows and agents to automate core FinOps activities, including anomaly detection, optimization recommendations, contract analysis, tagging enforcement, and forecasting support
- Reduce manual reporting and repetitive analysis through scalable automation and agentic operating models
- Establish guardrails and controls for autonomous or semi\-autonomous FinOps actions
Vendor and Commercial Management:
- Partner with Procurement, Finance, Architecture, and platform leaders on cloud, AI, and SaaS commercial strategy
- Use consumption and utilization data to support commitment planning, vendor negotiations, renewal strategy, and commercial restructuring
- Improve value realization from hyperscalers, model providers, and platform vendors through disciplined governance and measurable performance tracking
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:
- Undergraduate degree or equivalent experience
- 10\+ years of experience in FinOps, cloud economics, technology business operations, platform economics, or closely related technology leadership roles
- 5\+ years of leadership experience managing cross\-functional initiatives, programs, or teams in a large enterprise environment
- Demonstrated experience building a FinOps, cloud economics, or technology cost management capability at scale
- Solid understanding of public cloud cost drivers, including compute, storage, networking, commitments, reservation strategies, and utilization optimization
- Solid working knowledge of AI/ML workload economics, including model APIs, inference patterns, token\-based cost drivers, GPU/accelerator usage, or comparable AI platform economics
- Experience partnering with engineering, architecture, finance, procurement, and vendor teams to influence both technical and commercial decisions
- Proven ability to create executive\-ready reporting and translate technical spend patterns into business decisions
- Experience with dashboards, telemetry, and cost observability tooling that supports chargeback/showback, forecasting, and optimization
Preferred Qualifications:
- Experience building or deploying AI\-enabled automation, agents, or copilots in a production or enterprise setting
- Experience in FinOps using data to drive recommendations to lower costs and improve efficiency
- Experience leading major cloud commitment restructures or platform modernization efforts
- Familiarity with model selection tradeoffs, prompt/inference optimization, RAG economics, and agent runtime design considerations
- Solid point of view on the future of AI cost attribution, agent ROI, and cloud\-to\-AI spend convergence
- 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 to $230,800 annually based on full\-time employment. We comply with all minimum wage laws as applicable.
*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: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($182K) sits 16% below the category median. Disclosed range: $134K to $230K.
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.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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).
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 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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