Graduate Fellow - AI & Knowledge Mgt.

Savannah-River-National-Laboratory

Aiken (SC)

On-site

USD 60,000 - 90,000

Full time

4 days ago

Be an early applicant

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Benefits offered by this job

Relocation assistance

Healthcare benefits

Paid time off

Life Insurance

Job summary

Savannah River National Laboratory seeks a graduate fellow to advance AI-driven knowledge management, focusing on LLMs, RAG, and knowledge graphs. You will design pipelines, develop multi-agent architectures, and integrate AI tools into scientific workflows while authoring technical docs and contributing to a shared research codebase.

The role emphasizes collaboration across teams, staying current with AI developments, and applying methods to complex scientific challenges at SRNL in Aiken, SC.

Qualifications

Recent graduate (M.S. or Ph.D.) in Computer Science, Data Science, Information Science, or other scientific and engineering disciplines
Strong proficiency in Python, including experience with AI/ML libraries such as PyTorch, Hugging Face Transformers, or LangChain
Foundational understanding of large language models, prompt engineering, and retrieval-augmented generation (RAG)
Experience with or coursework in natural language processing (NLP) or knowledge representation
Ability to clearly document and communicate technical research, including writing reports and presenting findings

Responsibilities

Design and implement knowledge management pipelines using LLMs, RAG, and vector databases for intelligent information retrieval across large multi-modal corpora
Develop and evaluate multi-agent AI architectures for automated reasoning, summarization, and decision support
Build and maintain knowledge graphs and ontologies to represent complex domain relationships and support semantic search
Collaborate with cross-functional research teams to integrate AI knowledge tools into existing scientific workflows
Author technical documentation, scientific journal articles, and internal reports communicating methods and findings
Participate in code reviews and contribute to a shared, well-maintained research codebase
Monitor and evaluate emerging developments in LLMs, agentic AI, and knowledge management frameworks and assess applicability

Skills

Python

AI/ML libraries

LangChain

PyTorch

RAG

Knowledge representation

Documentation

Education

MS or PhD in Computer Science/Data Science/Info Science

Tools

Hugging Face Transformers

LangChain

Vector databases

Distributed knowledge graphs

Job description

Savannah River National Laboratory is seeking a highly motivated graduate fellow to advance our AI-driven knowledge management capabilities. This fellowship is focused on building next-generation systems for intelligent information retrieval, knowledge graph construction, and multi-agent AI workflows that support complex scientific workflows. The successful candidate will bring graduate-level research experience in large language models, retrieval-augmented generation (RAG), or knowledge representation, and a passion for applying these techniques to real-world challenges across scientific and engineering domains.

Design and implement knowledge management pipelines using large language models (LLMs), retrieval-augmented generation (RAG), and vector databases to enable intelligent information retrieval across large, multi-modal document corpora
Develop and evaluate multi-agent AI architectures for automated reasoning, summarization, and decision support
Build and maintain knowledge graphs and ontologies to represent complex domain relationships and support semantic search
Collaborate with cross-functional research teams to integrate AI knowledge tools into existing scientific workflows and applications
Author technical documentation, scientific journal articles, and internal reports communicating methods and findings to both technical and non-technical audiences
Participate in code reviews and contribute to a shared, well-maintained research codebase
Monitor and evaluate emerging developments in LLMs, agentic AI, and knowledge management frameworks, and assess their applicability to ongoing projects

Responsibilities

Design and implement knowledge management pipelines using large language models (LLMs), retrieval-augmented generation (RAG), and vector databases to enable intelligent information retrieval across large, multi-modal document corpora

Develop and evaluate multi-agent AI architectures for automated reasoning, summarization, and decision support

Build and maintain knowledge graphs and ontologies to represent complex domain relationships and support semantic search

Collaborate with cross-functional research teams to integrate AI knowledge tools into existing scientific workflows and applications

Author technical documentation, scientific journal articles, and internal reports communicating methods and findings to both technical and non-technical audiences

Participate in code reviews and contribute to a shared, well-maintained research codebase

Monitor and evaluate emerging developments in LLMs, agentic AI, and knowledge management frameworks, and assess their applicability to ongoing projects

Qualifications

Minimum Qualifications

Recent graduate (M.S. or Ph.D.) in Computer Science, Data Science, Information Science, or other scientific and engineering disciplines
Strong proficiency in Python, including experience with AI/ML libraries such as PyTorch, Hugging Face Transformers, or LangChain
Foundational understanding of large language models, prompt engineering, and retrieval-augmented generation (RAG)
Experience with or coursework in natural language processing (NLP) or knowledge representation
Ability to clearly document and communicate technical research, including writing reports and presenting findings

Preferred Qualifications

Research experience or publications related to LLMs, knowledge graphs, information retrieval, or multi-agent systems
Hands‑on experience building end‑to‑end RAG pipelines or agentic AI workflows
Familiarity with knowledge graph construction, ontology design, or semantic web technologies (RDF, SPARQL, OWL)
Experience with vector databases or embedding‑based search systems
Background in a scientific or national security domain (e.g., environmental science, bioengineering, chemistry) is a plus
Experience working in a research or government laboratory environment

Security Clearance Information
This position will require selected candidate to be able to obtain a DOE Security Clearance. BSRA is required by DOE to conduct a pre‑employment drug test and background review that includes checks of personal references, credit, criminal records, and employment history, and education verifications. Positions with BSRA require applicants to have the ability to obtain and maintain a DOE L or Q‑level security clearance, which requires U.S. citizenship. Factors such as pre‑employment background review results, dual citizenship status, and unpaid and/or unfiled taxes may impact your ability to obtain a security clearance. This list of factors is not exhaustive; pre‑employment information and security clearance requests are reviewed on a case‑by‑case basis.

About Us

"We put science to work!"

Savannah River National Laboratory (SRNL) is a multi-program laboratory applying state of the art science and practical, high-value, cost-effective solutions to complex technical problems to protect the nation. Located at the U.S. Department of Energy’s (DOE) Savannah River Site (SRS) in Aiken SC, the laboratory develops and deploys innovative technologies to address some of the nation’s environmental, energy, and national security challenges.

BSRA Savannah River Alliance (BSRA) is constantly assessing trends to provide the best possible benefits to our workforce. We also negotiate cost effective premiums that will meet the needs of our evolving workforce.

Some of the *Benefits offered to employees include:

*Benefits vary based upon employment status

Highly competitive Medical, Dental, and Vision options including HSA options with company provided seed
Short- & Long-Term Disability (company paid)
Life Insurance Non-Contributary 1X salary (company paid)
AD&D Non-contributary 1x salary (company paid)
Savings & Investment plan:
Qualified Non-Elective Company Contribution of 5% each pay period with immediate vesting
Company match 50 cents/dollar up to 8% (5 yrs. vesting in company match)
Contributory Life Insurance up to 5x Salary with $1M Cap
Contributory AD&D (employee, spouse and children)
Paid Time Off
Employee Assistance Plan
SRNL offers a competitive relocation package to ease the transition process. Domestic and international relocation assistance is available for certain positions.

For more information about our benefits, working here, and living here, visit the “About” tab at www.srnl.doe.gov .

BSRA is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or protected veteran status. BSRA is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process.

About the Team

SRNL’s Environmental & Legacy Management (ELM) Directorate closely collaborates with the Department of Energy (DOE) and site contractors to develop, mature, and apply science and technology needed to resolve environmental challenges and advance legacy management missions. As the lead laboratory for Environmental Management (DOE-EM) and Legacy Management (DOE-LM), SRNL applies our talent and expertise to develop and deploy innovative approaches and technologies to reduce risk, cost, and schedule of environmental cleanup and nuclear material processing. Additionally, ELM is using our competencies to develop new materials and processes for a range of clean energy applications.

Job Info