Cultural Studies Quality Assurance Lead (QAL)

Pay: up to $65/hour

In this hourly, remote contractor role, you will work as an Art History / Cultural Studies Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across art history, visual culture, cultural studies, and humanities AI training projects. You will review AI-generated art history/cultural studies content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.

You will assess work for historical accuracy, visual-analysis quality, cultural context, terminology accuracy, interpretive nuance, source awareness, representation sensitivity, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong art history/cultural studies expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your art history/cultural studies quality leadership will directly help improve the world’s premier AI models by ensuring that humanities training data is accurate, culturally sensitive, visually literate, historically grounded, well-explained, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important:

There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Responsibilities

• Quality monitoring: Spot-check art history/cultural studies items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Humanities review: Evaluate AI-generated art historical explanations, visual analyses, cultural comparisons, museum-style descriptions, critical interpretations, and context summaries for accuracy and nuance.
• Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and art history/cultural studies-specific review standards.
• Question handling: Respond to trainer/QA questions clearly and promptly, especially around visual analysis, attribution, periodization, cultural context, interpretive claims, representation, ethics, and rubric interpretation.
• Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
• Documentation: Create and maintain art history/cultural studies project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
• Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and art history/cultural studies-specific review requirements.
• Quality alignment: Ensure all trainers and QAs apply art history/cultural studies review guidelines consistently and understand updates as projects evolve.
• Bias and ethics review: Flag culturally insensitive, Eurocentric, decontextualized, stereotyping, misattributed, or unsupported claims about art, culture, artists, or communities.
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for art history/cultural studies AI training projects.

Requirements

• Bachelor’s, Master’s, or PhD degree in Art History, Cultural Studies, Visual Culture, Museum Studies, Humanities, Fine Arts, Comparative Literature, Media Studies, Anthropology, History, or a closely related field.
• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
• 3+ years of experience in art historical research, cultural analysis, museum/curatorial work, teaching, academic writing, visual analysis, editing, cultural criticism, or related humanities workflows.
• Strong understanding of art historical methods, visual analysis, iconography, style, periodization, patronage, medium/materials, museum ethics, cultural theory, representation, and historical context.
• Ability to evaluate art history/cultural studies content against detailed rubrics and identify issues such as misattribution, incorrect periodization, weak visual analysis, cultural stereotyping, unsupported interpretation, outdated terminology, or decontextualized claims.
• Familiarity with areas such as ancient art, medieval art, Renaissance art, modern/contemporary art, non-Western art histories, photography, film/media, visual culture, postcolonial theory, gender studies, museum studies, or heritage studies is preferred.
• Experience leading or supporting remote teams of researchers, writers, reviewers, educators, curators, annotators, or QAs is strongly preferred.
• Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, calibration tasks, and documentation.
• Experience with AI training, data annotation, LLM evaluation, humanities QA, cultural sensitivity review, image/content review, or rubric-based review is a strong plus.

Skills: Humanities Review, Cultural Studies, visual culture, AI Training, art history, Trainer Feedback, cultural context, museum studies, Visual Analysis, LLM evaluation

Open to candidates in: The United States

Interview language: English