Research Scientist, -Scale Data Science and Learning

Overview

The Analytics and AI Methods at Scale (AAIMS) group in the National Center for Computational Science (NCCS) is hiring a Research Scientist to advance the frontier of AI for science, including scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models on leadership‑class supercomputers.

Requisition

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 access to unmatched resources to prototype new ideas, run experiments, and translate methods into scientific impact.

• 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, Deep Speed, 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.

• Conduct research in AI/ML at scale, working with cutting‑edge HPC resources.
• Collaborate with senior researchers and domain scientists on AI methods and scientific applications.
• Contribute to peer‑reviewed publications, technical reports, and proposals.
• Engage in collaborative software development and open‑source contributions.
• Present research outcomes at conferences, workshops, and internal seminars.
• Contribute to a supportive, inclusive, and collaborative team culture.

• Ph.D. in Computer Science, Computer Engineering, or a field closely related to the job duties of this position.
• Demonstrated research in one or more areas of HPC or AI (e.g., large‑scale training, scientific reasoning, reinforcement learning, or distributed systems).
• Strong programming skills (Python, C/C++, or equivalent) and experience with ML frameworks (e.g., PyTorch).

• Experience with large‑scale experiments on HPC or cloud platforms.
• Strong publication record commensurate with career stage.
• Familiarity with distributed training frameworks (e.g., Deep Speed, Megatron‑LM, Ray).
• Demonstrated ability to work collaboratively in multidisciplinary research teams.
• Interest in developing open‑source tools and contributing to community efforts.

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80‑year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment.

These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions…