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About This Role
At UnitedHealthcare, we're simplifying the health care experience, creating healthier communities and removing barriers to quality care. The work you do here impacts the lives of millions of people for the better. Come build the health care system of tomorrow, making it more responsive, affordable and optimized. Ready to make a difference? Join us in making healthcare work better for everyone through people-led, responsible AI while Caring. Connecting. Growing together.
We are seeking a Sr. AI/ML Engineer with strong full stack development experience to join our LMIS team supporting UnitedHealthcare Employer & Individual (E&I) modernization and underwriting. In this role, you will design, build, and deploy secure, scalable applications that combine responsive user interfaces, backend services, APIs, data integrations, and cloud technologies with AI/ML, Large Language Models (LLMs), retrieval-augmented generation (RAG), and agentic AI capabilities. You will collaborate with engineering, architecture, product, and underwriting teams to modernize legacy systems, automate complex workflows, and deliver production-ready solutions that improve operational efficiency and the health benefits planning experience. Join us to start Caring. Connecting. Growing together.
Primary Responsibilities:
- Design, develop, and deploy AI-powered solutions and platforms to address complex business challenges with a solid emphasis on the responsible use of AI
- Design and implement retrieval-augmented generation (RAG) pipelines that integrate enterprise knowledge sources, vector search, embeddings, and LLM orchestration patterns
- Support agentic AI architectures by integrating Model Context Protocol (MCP) capabilities and agent-to-agent communication protocols for secure, interoperable workflow execution
- Leverage enterprise-approved AI tools to enhance productivity and innovation by streamlining workflows and automating repetitive engineering tasks
- Evaluate emerging trends and approved toolsets in the AI/ML landscape to inform solution design, architectural decisions, and strategic underwriting and modernization initiatives
- Design, develop, and optimize robust AI/ML capabilities using Python to address complex underwriting and benefit determination business needs
- Build, integrate, and maintain high-performance, secure API endpoints to expose machine learning models and LLM functionalities to core underwriting systems
- Integrate Large Language Models (LLMs) and advanced AI model architectures into production environments to enhance user workflows, benefit assessments, and operational efficiency
- Collaborate with software engineers, data engineers, and underwriting stakeholders to deliver scalable machine learning pipelines and microservices
- Ensure high availability, security, and performance of deployed models through rigorous testing, monitoring, and debugging
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:
- 5+ years of software engineering, system integration, or machine learning engineering experience
- 3+ years of hands-on full stack development experience using JavaScript or TypeScript, a modern front-end framework, backend services, RESTful APIs, and relational or NoSQL databases
- 3+ years of professional experience developing and deploying robust web APIs (such as FastAPI, Flask, or RESTful services) in production environments
- 3+ years of experience programming in Python, including standard machine learning libraries (such as NumPy, Pandas, Scikit-Learn, PyTorch, or TensorFlow)
- 1+ years of hands-on experience integrating or working with Large Language Models (LLMs) or generative AI concepts
- Experience designing or implementing retrieval-augmented generation (RAG) pipelines, including embeddings, vector databases, semantic search, and LLM orchestration
- Driver's License and access to a reliable transportation
Preferred Qualifications:
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related field (or equivalent years of experience)
- Experience in health insurance, financial services, or supporting underwriting systems and technologies
- Experience utilizing generative AI development tools (such as LangChain, Hugging Face, or prompt engineering frameworks) to build application solutions
- Experience with vector databases, knowledge retrieval patterns, prompt grounding, tool calling, or enterprise search integration
- Experience with cloud platform environments (e.g., Azure, AWS, GCP) and containerization tools (such as Docker or Kubernetes)
- Familiarity with CI/CD deployment pipelines and git-based source control workflows
- Experience with React or Angular, automated testing, secure coding, application monitoring, and integrating full stack applications with AI/ML or enterprise services
- Familiarity with agent orchestration frameworks, multi-agent systems, MCP servers/clients, and secure agent-to-agent communication design patterns
- Familiarity with MLOps frameworks, model performance monitoring, and model registries
- Proven excellent communication and collaboration skills with the ability to articulate technical concepts to both technical and non-technical stakeholders
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 $120,100 to $214,500 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 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.
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Salary Context
This $120K-$214K 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 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 $212,040 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 ($167K) sits 21% below the category median. Disclosed range: $120K to $214K.
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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