Associate AI/ML Engineer

UnitedHealth Group•Eden Prairie, MN

6h•$60,200 - $107,400•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 diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together. We are seeking an Associate AI/ML Engineer (early‑career) who is excited to grow in a supportive, collaborative environment. You’ll be set up for success with clear expectations, opportunities to pair with senior engineers, and mentorship as you learn‑by‑doing—contributing to AI services, exploring Generative AI, and building foundational skills across machine learning and data engineering. We value curiosity, steady progress, and thoughtful questions as much as technical output. You will enjoy the flexibility to telecommute from anywhere within the U.S. as you take on some tough challenges.

Requirements

Bachelor’s degree in Computer Science, or IT or Data Science, or a related field
1+ years of software engineering experience (including internships, co‑ops, academic, or personal projects) delivering well‑tested applications and services
6+ months of AI/ML engineering experience, including prototyping, evaluation, and supporting deployment/monitoring in collaboration with senior engineers
6+ months of hands‑on experience (coursework, internships, or projects) with Python and SQL; familiarity with Java and scripting is a plus
6+ months of experience with any one major cloud platform (AWS, Azure, or GCP) through internships, labs, or projects or professional experience

Nice To Haves

Familiarity with MLOps concepts (experimentation tracking, model packaging, deployment patterns, and monitoring basics)
Exposure to Infrastructure as Code tools such as Terraform or CloudFormation (learning‑level or project experience)
Awareness of data governance and access control concepts; familiarity with Databricks features like Delta Lake and Unity Catalog is a plus
Familiarity with common AI/ML libraries and basic patterns for integrating models into applications (inference, evaluation basics, and logging/monitoring fundamentals)
Exposure to big data technologies such as Spark; familiarity with ecosystem tools (e.g., Kafka/Hadoop) is a plus
Basic understanding of data science fundamentals (statistics/probability, metrics, and data modeling); deeper coursework or hands‑on project experience is a plus
Exposure to Databricks and Spark (PySpark/SparkSQL) through coursework, projects, or professional experience; familiarity with building basic data pipelines is a plus
Familiarity with distributed data processing concepts (batch/streaming fundamentals, data partitioning, and performance basics)
Foundational understanding of clean, maintainable code, debugging, and basic API/service concepts; eager to learn system design and modern engineering best practices
Ability to design, build, deploy, and operate production‑ready services with guidance, including exposure to CI/CD and cloud infrastructure

Responsibilities

Implement and iterate on AI and machine learning solutions (including Generative AI) by building prototypes, integrating models into services, and improving quality based on evaluation results
Support proof‑of‑concepts and model experiments and help baseline performance using clear evaluation metrics (with guidance on approach and tooling)
Contribute to training and inference pipelines using Databricks, PySpark, and cloud platforms (AWS, Azure, GCP) with guidance from senior engineers
Learn and apply GenAI building blocks such as RAG and prompt orchestration tools (e.g., LangChain); exposure to vector databases is a plus
Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI
Support performance and cost improvements (e.g., caching, batching, basic optimization), and learn model optimization techniques (quantization/distillation) as needed
Contribute to building and testing APIs (e.g., REST with FastAPI) and learn delivery fundamentals such as containers (Docker) and simple app frameworks (e.g., Flask/Streamlit) as needed
Collaborate with cross‑functional partners to understand business needs, ask clarifying questions, and communicate progress and results clearly
Participate in design and code reviews, learn from feedback, and contribute to team standards for quality, safety, and trust