Anthropology Quality Assurance Lead (QAL)

About the role

In this hourly, remote contractor role, you will work as an Anthropology Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across anthropology-focused AI training projects. You will review AI-generated anthropology 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.

Responsibilities

• Quality monitoring: Spot-check anthropology items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Anthropology review: Evaluate AI-generated anthropology explanations, ethnographic summaries, cultural comparisons, archaeology discussions, human evolution explanations, and social/cultural reasoning for accuracy and sensitivity.
• Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and anthropology-specific review standards.
• Question handling: Respond to trainer/QA questions clearly and promptly, especially around cultural context, anthropological concepts, ethnographic methods, archaeology, human evolution, ethics, representation, 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 anthropology 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 anthropology-specific review requirements.
• Quality alignment: Ensure all trainers and QAs apply anthropology review guidelines consistently and understand updates as projects evolve.
• Bias and ethics review: Flag ethnocentric, stereotyping, culturally insensitive, outdated, unsupported, or ethically problematic anthropology content.
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for anthropology AI training projects.

Requirements

• Bachelor’s, Master’s, or PhD degree in Anthropology, Cultural Anthropology, Archaeology, Biological Anthropology, Linguistic Anthropology, Sociology, Area Studies, Museum Studies, or a closely related field.
• 3+ years of experience in anthropology research, teaching, ethnography, archaeology, museum/heritage work, cultural analysis, academic writing, fieldwork, or related workflows.

Qualifications

• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
• Strong understanding of anthropological theory, cultural relativism, ethnographic methods, kinship, ritual, language/culture relationships, material culture, human evolution, archaeology, ethics, and representation.
• Ability to evaluate anthropology content against detailed rubrics and identify issues such as cultural stereotyping, ethnocentrism, unsupported generalizations, outdated terminology, methodological errors, weak contextualization, or ethically problematic framing.
• Familiarity with areas such as ethnography, participant observation, archaeological methods, human origins, kinship systems, religion/ritual, migration, colonialism, Indigenous studies, museum ethics, or linguistic anthropology is preferred.
• Experience leading or supporting remote teams of researchers, educators, reviewers, fieldworkers, annotators, or QAs is strongly preferred.

Skills

• Strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert teams.
• 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, social science QA, academic review, cultural sensitivity review, or rubric-based review is a strong plus.