Director of AI/ML Engineering Remote Nationwide or Hybrid in MN/DC

UnitedHealth Group•Eden Prairie, MN

5h•Remote

About The Position

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 AI/ML Engineering team within Optum Health Claims, part of Optum Technology, is redefining how healthcare claims are processed at massive scale. This team sits at the intersection of claims platforms, data engineering, observability, and applied AI, driving the transition from manual, rules‑driven operations to intelligent, touchless, and self‑healing claims systems. As Director of AI/ML Engineering, you will lead a transformation agenda, growing, high‑impact team responsible for building enterprise‑grade AI platforms, claims data pipelines, and operational intelligence capabilities that support tens of millions of claims annually. Your charter spans traditional ML, modern AI techniques, and automation, applied directly to real operational problems—reducing rework, improving accuracy, lowering SG&A, and accelerating outcomes. You will partner closely with claims engineering, product, data, operations, enterprise architecture, and business leaders across Optum and UnitedHealth Group. The work is hands‑on, highly visible, and outcome‑driven-ideal for a leader excited to build AI as a fabric across healthcare operations, not just models in isolation. The team operates at enterprise scale, with strong executive sponsorship, mature production platforms, and a mandate to innovate. If you are energized by turning complex data and workflows into measurable operational advantage, this role offers the opportunity to lead meaningful transformation in one of the most critical domains in healthcare. 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.

Requirements

6+ years of hands on experience with traditional ML techniques (e.g., regression, tree based models, NLP, classical optimization) and modern AI approaches (deep learning, GenAI/LLMs, RAG, agentic AI systems)
5+ years of deep experience in data engineering, including data quality management, feature pipelines, analytical pipelines, and ML data readiness at scale
5+ years of experience leading and scaling engineering teams (typically 10–40+ engineers/data scientists) delivering production AI/ML systems with clear ownership of reliability, cost, performance, and business outcomes
4+ years of proven ability to translate complex operational problems into production grade, scalable AI/ML solutions embedded in enterprise workflows
3+ years of solid, hands on experience with claims platforms or core transaction systems, with healthcare claims domain experience (e.g., adjudication, EDI, pricing, edits, payment integrity, post pay analytics)

Nice To Haves

Experience applying AI/ML to large scale operational workflows (claims, payments, servicing, or similar domains)
Experience building AI solutions with governance, compliance, and reliability in mind
Familiarity with cloud based AI/ML platforms and enterprise integration patterns
Proven exposure to observability, process mining, or operational analytics platforms
Proven solid executive communication skills with a track record of delivering measurable business impact

Responsibilities

Lead the design and execution of AI/ML engineering strategy for claims platforms, aligned with business outcomes
Build and operationalize enterprise data pipelines across multiple claims systems
Develop and scale observability and operational intelligence suites that surface manual work, rework, exceptions, and automation opportunities
Apply traditional ML, advanced AI, and intelligent automation to reduce manual claims processing and improve accuracy
Partner with engineering, product, data, and operations leaders to embed AI directly into end‑to‑end claims workflows
Establish engineering standards, MLOps practices, and responsible AI patterns for production systems
Mentor and grow senior engineers and AI practitioners in a high‑expectation, high‑impact environment