Assistant Research Scientist (PREP0004176)

General 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:

Reliability of Human and LLM Annotations for AI Risk Assessment

The work will entail:

This project focuses on using Large Language Models (LLMs) to provide annotations of evaluation data (a.k.a., LLM as judge), and the design of an Inter-Annotator Agreement study to assess the reliability of both human and LLM annotations. The candidate will explore assessing the indicators of a given AI-related risk, determining how to identify them, and providing annotators with examples to annotate the presence of various risks. The project aims to develop an annotation framework for AI risk assessment and establish metrics for data quality in AI risk research, supporting broader work at NIST in assessing and measuring the validity and reliability of AI-related risks in data annotation.

U.S. Citizen Preferred

Key responsibilities will include but are not limited to:

• Gain familiarity with existing literature on data annotation and LLM as judge
• Understand NIST's role and ongoing efforts in assessing and measuring the validity and reliability of AI-related risks in data annotation
• Contribute to developing an annotation framework for AI risk assessment
• Collaborate effectively with cross-functional and interdisciplinary stakeholders to ensure successful project outcomes

Deliverables

• Contributions to a NIST report that supports ongoing NIST AI evaluation efforts focused on the design of an Inter-Annotator Agreement to assess the reliability of both human and LLM annotations.

Qualifications

• Background in Computer Science, Data Science, or related field.
• Education level: Bachelor's or Graduate Degree
• Strong interest in data annotation and AI risks
• Familiarity with scientific reading and technical writing

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

Salary Range

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.