Senior AI/ML Engineer - Remote NLP & LLMs for Health Care

Optum; San Francisco (CA); Remote; USD 146,000 - 250,000; Full time

Job summary

UnitedHealth Group’s Optum AI team seeks an accomplished AI/ML scientist to advance LLM and NLP capabilities. You’ll develop scalable models for real-time processing and contribute to responsible AI standards.

You will collaborate with world‑class researchers, define strategic priorities for deep learning, RAG and LLMs, and help drive adoption of new science across service teams while working remotely within the U.S.

Qualifications

• Master's degree in computer science or related quantitative field.
• 5+ years of experience using ML to solve NLP, multimodal RT processing, or other AI domains.
• 3+ years of DL frameworks such as PyTorch and/or TensorFlow.
• 3+ years developing ML applications.
• 3+ years experience in Responsible AI metrics, best practices and/or solutions.
• 3+ years ML and RL research and development experience.

Responsibilities

• Develop models and methods that extend and improve LLM capabilities and reliability.
• Research and implement scalable machine learning models for real-time processing.
• Developing new solutions and science best practices in Responsible AI.
• Developing our research and innovation agenda.
• Defining the strategies, priorities, and metrics for technical progress in Deep Learning, RAG and LLMs.
• Collaborating with the Optum AI team and fellow scientist and engineers.
• Driving adoption of new science and best practices among service teams.

Skills

Machine Learning

NLP

RL Research

Education

Master's degree in computer science

Ph.D. in Machine Learning

Tools

PyTorch

TensorFlow

Job description

UnitedHealth Group’s Optum AI team seeks an accomplished AI/ML scientist to advance LLM and NLP capabilities. You’ll develop scalable models for real-time processing and contribute to responsible AI standards.

You will collaborate with world‑class researchers, define strategic priorities for deep learning, RAG and LLMs, and help drive adoption of new science across service teams while working remotely within the U.S.