Research Associate in AI Risk Assessment and LLM Annotation George Washington University in United States

The George Washington University, in collaboration with NIST through the Professional Research Experience Program (PREP), is seeking a Research Associate to contribute to cutting-edge research in AI risk assessment and large language model (LLM) annotation. This position is ideal for candidates passionate about trustworthy AI, data quality, and the evaluation of AI systems. The project centers on the reliability of human versus LLM annotations for AI risk assessment, with a focus on designing inter-annotator agreement studies and developing frameworks for measuring data quality in AI risk research.

As a Research Associate, you will assess indicators of AI-related risks, design studies to compare the reliability of human and LLM annotations, and contribute to official NIST reports on AI evaluation. The role involves close collaboration with the NIST team and cross-functional stakeholders in AI safety. Applicants should have a Bachelor’s or Graduate Degree in Computer Science, Data Science, or a related field, and demonstrate a strong interest in data annotation and AI risks. Familiarity with scientific reading and technical writing is required, and candidates must be eligible to obtain a U.S. Department of Commerce background check.

This opportunity offers the chance to work at the intersection of AI safety, data science, and public policy, contributing to the development of trustworthy AI systems. The position is based at George Washington University, with collaboration with NIST, and is part of a professional research program. While specific funding details are not provided, the role is a paid research associate position. Interested candidates should apply via the provided application link and ensure they meet all eligibility requirements.