Lead Research Scientist, Foundation Models & Agentic Systems - Remote

UnitedHealth Group•Annapolis Neck, MD

15d•$112,700 - $193,200•Remote

About The Position

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. Optum AI is building foundation models and agentic systems that can understand complex healthcare data, reason about clinical workflows, and safely assist clinicians and patients at scale. We’re looking for a lead research scientist who wants to push the frontier of: Pretraining and post‑training of LLMs/SLMs (including RL, RLHF, RLAIF), and Agentic systems that plan, use tools, and operate in real healthcare workflows This role is ideal for a researcher who enjoys turning open‑ended ideas into state‑of‑the‑art models, publishing in top venues, and seeing their work deployed to improve health outcomes for millions of people. 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.

Requirements

PhD or equivalent experience in Computer Science, Machine Learning, Statistics, Electrical Engineering, or a related quantitative field
7+ years of hands‑on experience (including PhD research and post-doctoral research) building and evaluating NLP models using deep learning frameworks (preferably PyTorch) and libraries such as Hugging Face Transformers
2+ years of experience with modern NLP / GenAI approaches, such as: LLMs and SLMs (fine‑tuning, evaluation, prompt engineering, safety) Text embeddings, classification, and retrieval-augmented generation (RAG) Distributed or large‑scale training of generative models
Demonstrated solid research background in machine learning, with emphasis in at least one of: Reinforcement learning / RLHF / RLAIF Natural language processing / large language models Agentic systems, planning, or tool‑using agents
Proven experience leading research projects (e.g., as a primary author on publications, leading subprojects in your research group, or driving technical direction on an industry or internship project)

Nice To Haves

Publication record in top‑tier AI/ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, KDD, AAAI), ideally including work related to LLMs, RL, or agentic systems. Healthcare‑related publications
Experience building RLHF or RLAIF pipelines end‑to‑end (e.g., reward modeling, feedback collection, policy optimization)
Experience with agent frameworks (e.g., tool‑use, planning, workflow orchestration) or multi‑agent systems
Experience working with large‑scale, real‑world data (healthcare, finance, recommendation systems, etc.)
Demonstrated familiarity with safe and responsible AI practices, especially in high‑stakes domains.
Experience mentoring junior researchers or engineers

Responsibilities

Lead research on pretraining and post‑training of language models
Design and train domain‑specialized LLMs and smaller language models (SLMs) for healthcare applications
Explore novel pretraining objectives, architectures, and data curation strategies
Develop post‑training pipelines (RL, RLHF, RLAIF) to align models with clinical best practices, safety guidelines, and user preferences
Develop RL/RLHF/RLAIF methods at production scale
Design reward models and feedback collection strategies with clinicians and domain experts
Implement and evaluate RL‑based fine‑tuning of foundation models using human and AI feedback
Work with platform teams to run large‑scale, distributed experiments on modern GPU/cloud infrastructure
Build and study agentic systems for healthcare
Design agent architectures that can plan, call tools/APIs, interact with retrieval systems (e.g., RAG), and handle multi‑step clinical workflows
Investigate memory, planning, and tool‑use strategies to make agents reliable, controllable, and debuggable
Define and evaluate benchmarks for agent performance, robustness, and safety in healthcare contexts
Drive research to production and publish your work
Own the end‑to‑end lifecycle of research projects: problem formulation, literature review, experimentation, evaluation, and iteration
Collaborate with product teams to translate research into deployed systems that support clinicians, care managers, and patients
Publish findings in top‑tier AI and ML venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, KDD, MLHC, CHIL) and present internally to Optum AI and business leaders
Ensure safety, fairness, and responsible AI
Closely collaborate with the Responsible Use of AI (RUAI) team to embed safety, fairness, and compliance into model and agent design
Develop evaluation protocols, diagnostics, and documentation to support model governance and regulatory requirements
Contribute to high‑quality engineering and collaboration
Build robust training and evaluation pipelines using Python, PyTorch/TensorFlow, and modern ML tooling
Follow best practices for software development (tests, packaging, GitHub, code review)
Communicate clearly with cross‑functional teams and stakeholders through written documents, technical talks, and presentations