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Graduate Fellow — AI & Knowledge Management

Savannah River National Laboratory

Position Overview

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.

 

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

 

Typical Tools & Technologies

AI & Knowledge Management

  • LLM frameworks: LangChain, LlamaIndex, Hugging Face Transformers
  • Vector databases: ChromaDB, Weaviate, Pinecone, FAISS
  • Knowledge graph tools: Neo4j, RDFLib, Apache Jena
  • Embedding and retrieval: OpenAI APIs, sentence-transformers, MTEB benchmarks
  • Agent orchestration: LangGraph, AutoGen, CrewAI

 

Core Development

  • Python libraries: NumPy, pandas, PyTorch, scikit-learn
  • NLP libraries: spaCy, NLTK, Hugging Face Datasets
  • Data visualization: Plotly/Dash, Matplotlib
  • Version control and collaboration: Git, GitHub/GitLab

 

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
  • Self-directed with strong collaboration and interpersonal skills

 

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