History Quality Assurance Lead (QAL)

As a History Quality Assurance Lead, you will review AI-generated historical content and work submitted by trainers and quality reviewers. You will use project guidelines and detailed rubrics to assess accuracy, context, balance, clarity, and evidence.

You will help keep history-focused AI training work consistent by identifying serious or repeated quality issues, explaining problems clearly, and supporting better review practices. The work covers chronology, sources, causation, cultural context, interpretation, and historical framing.

• Review historical explanations, timelines, comparisons, summaries, source-based answers, and reasoning.
• Identify anachronisms, incorrect chronology, unsupported claims, fabricated citations, biased framing, oversimplification, and misleading causal explanations.
• Spot-check history items and trainer or QA work, then provide precise written feedback and escalate concerns when needed.
• Answer questions about sources and interpretation, and communicate changes to guidelines and review standards.
• Create and maintain style guides, FAQs, trackers, quality notes, examples, calibration tasks, and onboarding materials.
• Support onboarding and training while finding ways to improve recurring quality gaps and QA workflows.

This is a remote, part-time contractor opportunity open to candidates in the United States. The role requires at least 20 hours per week and clear written English.

• Pay: Up to $50 per hour.
• Location: Open to candidates in the United States.
• Schedule: 20+ hours per week.
• Education: A bachelor's, master's, or PhD degree in History, Classics, Area Studies, Archaeology, Political History, Cultural History, International Relations, Humanities, or a closely related field.
• Experience: At least three years in historical research, teaching, writing, editing, academic review, museum or archival work, curriculum development, or a related humanities workflow.
• Knowledge: Strong understanding of historical methods, chronology, primary and secondary sources, historiography, causation, regional context, and evidence-based interpretation.
• Skills: Careful attention to detail, organized communication, and the ability to apply rubrics and explain historical issues precisely.
• Helpful experience: A historical specialization, remote team support, education or QA work, AI training, data annotation, language model evaluation, academic QA, fact-checking, or rubric-based review.
• Tools: Comfort with Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems can support the work.

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

AI training is the human work behind artificial intelligence, including reviewing model answers and preparing examples that help systems improve. People with strong subject knowledge are paid to judge accuracy, context, and quality so AI systems produce more useful and reliable results.