Join Optum AI, UnitedHealth Group's enterprise AI team, at the forefront of transforming healthcare through cutting-edge AI and ML. We are seeking an experienced Manager AI/ML Engineering with a background in Life Sciences or Diagnostics to lead the development and deployment of innovative solutions. Our team leverages vast datasets and resources to create high-impact AI applications across UnitedHealth Group's diverse businesses. You will contribute to groundbreaking work in ML, NLP, and LLMs, including generative AI methods tailored for healthcare data.

This role offers the opportunity to partner with world-class experts, potentially leading to patents and publications. We are committed to advancing health optimization globally through responsible AI/ML innovation and developing a robust enterprise AI/ML development platform.

Lead the design, development, and deployment of advanced Generative AI and NLP solutions utilizing state-of-the-art transformer models and LLMs. Evaluate and integrate new LLMs, vector search technologies, and prompt engineering techniques. Build and optimize Retrieval-Augmented Generation (RAG) systems with vector databases to enhance grounding and accuracy. Architect and implement agentic AI workflows for complex reasoning and planning. Drive the development of high-quality, scalable NLP pipelines and perform advanced data analysis. Optimize model inference, latency, and throughput, and lead experimentation cycles for data-driven decisions. Collaborate with cross-functional teams, author technical documentation, and mentor junior engineers on responsible AI practices. Support the deployment of production-grade GenAI systems, focusing on monitoring, safety, and reliability. Ensure compliance with company policies and directives.

A Bachelor's degree in Computer Science or Engineering with a focus on language processing is required, alongside 10+ years of overall experience and 7+ years in AI, ML, and/or NLP R&D. Demonstrated impact in the field is preferred. Experience with both traditional ML algorithms and modern deep learning/generative AI models is essential. Proficiency in Python, R, SQL, and cloud development environments like Azure, AWS, or GCP is mandatory. Strong analytical and problem-solving skills are crucial. Familiarity with repository management, GPU usage, and setting up pipelines for rapid testing is necessary. Experience in Life Sciences or Diagnostics domains, including areas like Clinical Trials, FDA Dossier Preparation, Drug Discovery, LIMS, Lab Diagnostics, or Patient Schedule Optimization, is highly advantageous and may relax the experience requirement for PhD candidates.