Research Scientist, Large-Scale Data Science and Learning

UT-Battelle; Oak Ridge (TN); On-site; USD 140,000 - 170,000; Full time

Benefits offered by this job

Relocation assistance

On-site amenities

Job summary

UT-Battelle's Oak Ridge National Laboratory (ORNL) invites applications for a Research Scientist in the Analytics and AI Methods at Scale (AAIMS) group at NCCS. The role focuses on advancing AI for science, including reasoning, federated learning, RL for self-improving models, and applying these methods on leadership-class supercomputers.

You will design, train, and evaluate AI systems that plan, reason, and act to accelerate discovery across domains such as materials, chemistry, climate,

Qualifications

• PhD in CS/CE or closely related field.
• Proven HPC/AI research with large-scale training or distributed systems.
• Strong Python, C/C++, and ML framework experience (e.g., PyTorch).

Responsibilities

• Design, train, and evaluate AI systems at scale on HPC resources.
• Collaborate with researchers and domain scientists on AI methods and applications.
• Publish findings and contribute to open-source projects.

Skills

PhD in Computer Science/Engineering

HPC/AI research experience

Python

C/C++

PyTorch

Education

PhD in Computer Science or related field

Tools

PyTorch

DeepSpeed

Megatron-LM

Job description

Overview

Requisition Id 16411

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.

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.

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.

Major Duties And Responsibilities

• 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.

Basic Qualifications

• 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).

Preferred Qualifications

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

Special Requirements

Please submit two letters of reference when applying to this position.

About ORNL

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 for energy, environmental, and security challenges facing the nation.

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well‑being of you and your family. Employee amenities such as on‑site fitness, banking, and cafeteria facilities are also available for added convenience.

• Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: [e-mail].

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT‑Battelle is an E‑Verify employer.