Senior Research Scientist, AI for Science

Oak Ridge National Laboratory•Oak Ridge, TN

54d

About The Position

The Analytics and AI methods at Scale (AAIMS) group in the National Center for Computational Science (NCCS) is hiring Senior Research Scientist to push the frontier of AI for science such as: scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models on leadership-class supercomputers. You'll help design, train, and evaluate AI systems that plan, reason, and take actions to accelerate discovery across domains (materials, chemistry, climate, fusion, biology, and more). NCCS operates the Frontier exascale supercomputer and world-class data facilities. This role sits at the intersection of AI at scale and HPC, giving you unmatched resources to prototype new ideas, run large ablations, and translate methods into scientific impact. Examples of Focus Areas: Agentic AI for Science: Autonomous and tool-using agents for experiment design, simulation steering, data collection, and lab/compute orchestration; planning and memory; multi-agent collaboration. Scientific Reasoning: Program/path-of-thought, tool-augmented and retrieval-augmented reasoning; uncertainty quantification and calibrated decisions. RL & Self-Improving Models: RLHF/RLAIF, online RL, self-play, open-ended discovery, reward modeling, curriculum/active learning, data selection, iterative post-training, safety alignment and guardrails. Foundation Models for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation pipelines for reasoning and agents. Federated & Collaborative Learning: Cross-silo training across institutions and facilities; privacy-preserving learning (secure aggregation, differential privacy, MPC/HE); personalization under heterogeneity; governance-aware data/model sharing; collaborative evaluation The NCCS is the home of the world's first exascale supercomputer Frontier. Our Leadership Computing Program (OLCF) provides world class computing facilities to applications across all computational domains and disciplines. We are an inclusive dynamic environment that welcomes those with initiative and creativity.

Requirements

PhD in Computer Science, Computer Engineering, or a field closely related to the job duties of this position.
A minimum of 6 years of relevant research experience outside of Ph.D.
Demonstrated research in cross-cutting fields of HPC and/or AI.

Nice To Haves

Demonstrated leadership in conceiving, planning, and delivering large-scale HPC-AI projects with measurable scientific or technological impact.
Experience securing competitive funding (e.g., DOE, NSF, DARPA, industry consortia) and leading multi-institution proposals.
Recognition by the broader community - invited talks, keynote addresses, professional society awards, or major benchmarks.
Impactful open-source contributions to HPC or AI framework (e.g., Megatron-LM, DeepSpeed, Ray, Distributed RL)
Interdisciplinary collaboration experience - working with domain scientist (climate, material, fusion, biology) to translate methods into real discoveries.
Strategic vision - ability to identify long-term research directions at the intersection of AI, HPC and domain sciences.

Responsibilities

Develop and coordinate division activities in HPC-AI with cross-cutting initiatives in the laboratory by establishing forward-looking centers of excellence.
Lead and collaborate with internal and external researchers on a variety of extreme-scale AI/ML research and projects.
Lead in authoring peer reviewed papers, technical papers, reports, and proposals. Advance personal and staff contributions in leading professional, academic, and research organizations.
Advance personal and staff contributions in leading professional, academic, and research organizations.
Team Building & Mentorship: Provide mentorship to postdocs, students, and junior staff, fostering long-term career development.
Stakeholder Engagement: Effectively communicate vision, strategy, and progress to DOE sponsors, industrial partners, and international collaborators.