Lead AI/ML Engineer - Remote

UnitedHealth Group•San Francisco, CA

23h•$145,500 - $249,500•Remote

About The Position

As a Lead AI/ML Engineer on the AI Platform Team within Optum AI, you will drive the design and development of scalable AI systems to improve patient care. This is a highly hands-on, technical engineering lead role focusing on technical execution, architectural strategy, and technical mentorship of other engineers. Our team delivers complete end-to-end solutions, from healthcare data ingestion and generative agents to AI model deployment, clinical applications, and analytics. Our current initiatives involve both leveraging and building cutting-edge Generative AI and Agentic AI technologies from scratch. We have the data and resources to make an impact on a massive scale, benefiting millions of people globally. 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

Bachelor's degree in Computer Science, Software Engineering, Information Technology, Mathematics, Statistics, or a related quantitative field, or 4+ years of equivalent software engineering experience in lieu of a degree
12+ years of experience in Software Engineering, Data Science, or Analytics, with at least 3+ years dedicated to AI/ML engineering or related fields
4+ years of experience in a team lead or technical leadership role (guiding technical projects, architectures, and mentoring engineers)
4+ years of hands-on Python programming experience
4+ years of hands-on experience in modern cloud infrastructure (e.g., AWS, Azure, or GCP)
1+ years of experience leveraging Large Language Models (LLMs) or developing generative AI solutions
Experience designing and delivering at least two complete AI products from ideation to production/release

Nice To Haves

Master’s degree or Ph.D. in Computer Science, Data Science, AI, or a related field
Hands-on experience working with Google Cloud Platform (GCP)
Experience with healthcare data standards and interoperability protocols (e.g., FHIR, HL7, HIPAA compliance)
Proven knowledge of CI/CD principles with experience designing and operating multiple production-grade pipelines
Experience with the challenges of delivering and monitoring LLM-based cloud systems in a production environment
Proficiency with GitHub and GitHub Actions, artifact and dependency management, containerization (Docker), and Infrastructure-as-Code (Terraform) to enable reliable, repeatable, and secure deployments
Demonstrated passion for innovation, staying current with emerging AI/ML trends, and a solid bias for action
Solid communication and presentation skills with the ability to explain complex technical concepts to diverse, non-technical audiences

Responsibilities

Lead and contribute to the design, hands-on implementation, and deployment of end-to-end AI/ML systems that enable the business to derive critical insights from patient data in a scalable, reliable, and cost-effective manner
Partner with product managers, data scientists, software engineers, and business stakeholders to translate business requirements into scalable AI systems
Design, develop, and deploy AI-powered solutions to address complex business challenges with a solid emphasis on the responsible use of AI
Leverage enterprise-approved and AI-assisted tools to streamline engineering workflows, automate routine tasks, conduct code reviews, perform testing, and drive continuous improvement
Ensure the delivery of high-quality, maintainable, and efficient code and technical specifications, leading by example to deliver product quality the first time
Drive architectural decisions and contribute to our long-term technical strategy for AI/ML systems, evaluating emerging trends and research to inform solution design
Own the full lifecycle of AI/ML initiatives, from problem definition and data exploration to agent development, deployment, and active monitoring
Define project scope, success metrics, and delivery milestones in collaboration with cross-functional teams, executing with solid engineering rigor through CI/CD, environment readiness, and quality gates
Stay ahead of the curve on emerging AI/ML technologies, tools, and research, promoting a culture of innovation through rapid iteration, experimentation, and data-driven decision-making
Act as a key technical resource and trusted advisor on complex or critical issues, actively reviewing the work of others and mentoring engineers to help them reach their full potential without direct administrative/HR management responsibilities