Description
PREP Research Associate
This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.
Research Title:
Citation Injector: Source Attribution for AI-Generated Text
The work will entail:
Overview: ITL's role in the Citation Injector project, "Citation Injector: Source Attribution for AI-Generated Text," involves the following tasks: (1) segmenting AI-generated text into attribution units and retrieving candidate source chunks from a local corpus, and (2) validating and reranking candidate evidence to insert accurate, source-grounded citations and produce auditable quality metrics. Our success depends upon the availability of highly skilled domain experts. We are challenged with difficult tasks that require not only expertise in building agentic AI and LLM pipelines, but also in designing LLM-judge techniques for narrow entailment and support/refutation checks.
U.S. Citizen Preferred
Key responsibilities will include but are not limited to:
§ Developing agentic AI evaluations and LLM pipelines to improve grounding, reliability, and traceability of AI-generated text.
§ Building citation reconstruction pipelines that convert uncited AI outputs into source-grounded, cited answers.
§ Designing LLM-judge techniques for narrow entailment and support/refutation checks to validate citation quality.
§ Produce high-quality publications based on research and results present at internal and external meetings and conferences.
Qualifications
• § US citizenship is preferred.
§ An MSc degree in Computer Science or a related field, with experience in AI/LLM system development.
§ Experience building and evaluating agentic AI systems and large language model (LLM) pipelines, including retrieval-augmented generation (RAG) and LLM-based judge/entailment evaluation.
§ Ability to build deployable software solutions for AI-based source attribution and evaluation tasks.
§ Strong oral and written communication skills and strong presentation skills.
Application Instructions
Please upload the following with your application:
• CV/Resume
*Please limit C.V to 3 pages only and ONLY include a valid email address for your contact info. Your resume will not be considered if the following information is included on your CV/resume.
• Self portraits
• Phone number
• Home address/Country
• Citizenship status
• Languages spoken
• Sex/Gender
