Lead AI/ML Software Engineer

Role Description

Optum AI is UnitedHealth Group’s enterprise AI team. We are AI/ML scientists and engineers with deep expertise in AI/ML engineering for health care. We develop AI/ML solutions for the highest impact opportunities across UnitedHealth Group businesses including UnitedHealthcare, Optum Financial, Optum Health, Optum Insight, and Optum Rx. In addition to transforming the health care journey through responsible AI/ML innovation, our charter also includes developing and supporting an enterprise AI/ML development platform.

You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

• Have impact at scale: We have the data and resources to make an impact at scale. When our solutions are deployed, they have the potential to make health care system work better for everyone.
• Do ground-breaking work: Many of our current projects involve cutting edge ML, NLP and LLM techniques. Generative AI methods for working with structured and unstructured health care data are continuously being developed and improved. We are working in one of the most important frontiers of AI/ML research and development.
• Partner with world-class experts on innovative solutions: Our team members are developing novel AI/ML solutions to business challenges. In some cases, this includes the opportunity to file patents and publish papers about the methods we develop. We also collaborate with AI/ML researchers at some of the world’s top universities.

Qualifications

• Bachelor of Science in computer science or related quantitative field
• 7+ years of professional software engineering experience, with at least 5+ years building and operating production ML systems
• Expert-level Python experience, including writing production-grade, testable, and maintainable code
• Hands-on experience building and deploying APIs using FastAPI (or similar Python web frameworks) in a production environment
• Solid experience deploying, managing, and scaling services in Kubernetes, including:
  • Containerization (Docker)
  • Helm or equivalent deployment tooling
  • Resource tuning, autoscaling, and failure recovery
• Proven experience with MLOps practices, including:
  • Model deployment and versioning
  • Monitoring model performance, drift, and system health

Requirements

• Experience with AzureML, DataBricks, and Azure Kubernetes
• Experience with distributed computing approaches
• Expertise in ML Ops, for example, model monitoring, drift detection, and event-based model retraining
• Proven solid technical communication skills
• Proven ability and drive to learn independently
• Proven problem-solving and analytical thinking
• Proven ability to explain technical decisions to stakeholders

Benefits

• Comprehensive benefits package
• Incentive and recognition programs
• Equity stock purchase
• 401k contribution (all benefits are subject to eligibility requirements)