Art History Quality Assurance Lead

You will review AI-generated humanities content and trainer quality assurance work. Your reviews will focus on accuracy, interpretive nuance, consistency, terminology, historical context, and cultural sensitivity.

• Review art history, visual culture, cultural studies, and humanities AI training items.
• Evaluate visual analyses, cultural comparisons, museum-style descriptions, critical interpretations, and context summaries.
• Check trainer and quality assurance submissions against project rubrics and quality standards.
• Write clear feedback and escalation notes for remote contributors.
• Update guidelines, FAQs, trackers, documentation, and quality notes as projects change.
• Support onboarding and calibration for contributors.
• Identify recurring issues and help improve quality assurance workflows across expert teams.

What It Pays and Takes

This is a part-time contractor role for an expert in art history, cultural studies, visual culture, museum studies, or a closely related humanities field.

• Pay: $65 per hour.
• Time requirement: 20+ hours per week.
• Location: United States.
• Language: Strong written English.
• Level: Expert.
• You must be able to assess visual analysis, historical context, representation, and culturally sensitive claims with care.
• You should be comfortable giving structured written feedback.
• Experience with AI training, large language model evaluation, humanities quality assurance, or rubric-based review is a strong plus.
• You should be comfortable using remote collaboration tools such as Discord, Google Sheets, and Google Docs.
• Helpful experience includes supporting remote teams of researchers, writers, reviewers, educators, curators, annotators, or quality assurance staff.
• Familiarity with style guides, onboarding materials, calibration tasks, and documentation workflows is helpful.

How It Works

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

About AI Training Work

AI training is the human work behind systems that generate and understand content. Reviewers assess examples, explain quality problems, and provide feedback that helps AI produce more accurate, useful, and responsible results.