Sr. AI/ML Engineer - Remote

UnitedHealth Group•Richardson, TX

1d•$120,100 - $214,500•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 Optum Health Technology team is seeking a Senior AI/ML Engineer with a strong data science background to build and scale cutting-edge AI-powered solutions that transform value-based care delivery. In this role, you will lead the design, development, statistical analysis, and enterprise deployment of machine learning models, generative AI architectures, predictive analytics, and intelligent automations that directly improve patient health outcomes and operational efficiency. You will collaborate closely with software engineers, data scientists, data architects, product managers, and clinical leaders to translate complex clinical data into actionable intelligence, streamline healthcare workflows, empower clinical decision-making, and deliver scalable value across the care continuum. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

Requirements

5+ years of software engineering, machine learning engineering, or data science experience in a production enterprise environment
3+ years of hands-on experience designing, statistical modeling, training, evaluating, and deploying machine learning or deep learning models in production
3+ years of proficiency with Python and core AI/ML and data science libraries (e.g., PyTorch, TensorFlow, scikit-learn, Pandas, NumPy, SciPy)
2+ years of experience with cloud platform infrastructure (AWS, Azure, or GCP) and cloud-native AI/ML/data services (e.g., SageMaker, Azure ML, Vertex AI)
2+ years of experience in statistical analysis, predictive modeling, hypothesis testing, and advanced data manipulation on complex datasets
2+ years of experience implementing MLOps, CI/CD pipelines, containerization (Docker, Kubernetes), and model monitoring/governance frameworks

Nice To Haves

Master’s or Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative field
Experience working with Large Language Models (LLMs), Generative AI architectures, prompt engineering, and Retrieval-Augmented Generation (RAG) frameworks
Experience with distributed data processing systems and analytics platforms such as Apache Spark, Databricks, or Snowflake
Background in healthcare technology, clinical data standards (FHIR, HL7), or working within highly regulated data compliance environments (HIPAA, HITRUST)
Proven track record of leveraging modern AI coding tools and automation frameworks to accelerate software and data science delivery

Responsibilities

Design, develop, and deploy AI-powered solutions to address complex healthcare challenges with an emphasis on the responsible and ethical use of AI
Perform exploratory data analysis (EDA), feature engineering, statistical modeling, and hypothesis testing on large-scale clinical and healthcare datasets
Leverage enterprise-approved AI tools and frameworks to streamline engineering workflows, automate repetitive tasks, and drive continuous process improvement
Evaluate emerging AI/ML trends, foundational models, statistical techniques, and technical tools to inform architectural solution design and strategic innovation
Build, optimize, and maintain production-grade machine learning pipelines and predictive models for data preprocessing, model training, evaluation, and monitoring
Architect scalable microservices and APIs to integrate machine learning models and data science capabilities seamlessly into enterprise applications and clinical workflows
Collaborate with cross-functional technical, data, and clinical teams to translate high-level business objectives into robust, secure, and performant solutions
Implement MLOps best practices to ensure continuous integration, automated model evaluation, governance, model tracking, and reliable deployment at enterprise scale