Records Manager for AI Training

The work

You will help evaluate AI-generated content related to records management, archival records, information management, collections, and library work. Your feedback will show where model responses are accurate, relevant, and consistent with professional practice.

The work is remote and asynchronous. You will work independently while contributing professional judgment to AI training research.

• Write prompts based on real records-management and archival responsibilities.
• Review AI-generated responses for accuracy, relevance, and professional standards.
• Assess content about classification systems, archival databases, reference services, historical-document appraisal, cataloguing, preservation, and collection development.
• Explain your decisions clearly through structured feedback.

What it pays and takes

• Pay: Up to $75 per hour.
• Schedule: 20 or more hours per week, with flexible asynchronous participation.
• Location: Open to candidates in the United States.
• Language: Professional fluency in English.
• Experience: At least four years in records management, archival work, information management, collections management, or librarianship.
• Skills: Experience organizing and retrieving archival records, maintaining classification systems, or managing records databases.
• Knowledge: Familiarity with reference services, historical-document authentication or appraisal, cataloguing, preservation, or collection development.
• Communication: Strong written communication and the ability to explain professional judgments clearly.
• Relevant background: Directing cataloguing or preservation work, coordinating educational programs, researching and acquiring archival materials, or managing collections.
• AI experience: No prior AI experience is required.

How it works

Apply on OpenTrain with your resume and then complete the application on the hiring site.

About AI training work

AI training is the human work behind systems that generate and evaluate text. People write examples, review model responses, and explain what is accurate or useful, so professional experience helps models handle real workplace tasks and language more reliably.