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Senior Ai/Ml Software Engineer

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.

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.

As a Senior AI/ML Software Engineer, you will be responsible for the design and implementation of cutting-edge AI/ML systems for real-world healthcare challenges at Optum; including problems around improving patient engagement through smart nudging, predicting onset and progression of disease, Large Language Model solutions for summarizing large scale patient information, and optimizing clinical trials using both structured and unstructured (text) data. As a senior engineer in charge of AI systems, you will apply state-of-the-art approaches for MLOps to ensure the performance, scalability and reliability of machine learning models serving our customers and build data pipelines that deliver real-time insights.

Optum AI team members:

• 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

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.

Primary Responsibilities:

• Own the inference and delivery of machine learning insights to multiple customers
• Manage, monitor, and scale machine learning systems, delivering insights to multiple customers
• Partner with cross-functional engineering teams to implement scalable, flexible pipelines that bring AI innovations into real-world products
• Work with large-scale data and distributed computing frameworks to process and analyze high-volume healthcare datasets efficiently
• Drive pipeline efficiency and reliability through profiling, refactoring and advanced engineering
• Develop new data pipelines to improve ML model usage

Required Qualifications:

• Bachelor of Science in computer science or related quantitative field
• 5+ years of professional software engineering experience, with at least 3+ 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

Preferred Qualifications:

• 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