History Quality Assurance Lead

Role overview

This is a remote contractor position focused on leading quality assurance for history-related AI training work. The role centers on reviewing history content produced by AI and by human trainers/QAs, making sure it matches project standards for accuracy, consistency, and clarity. You will also help coordinate quality workflows across a distributed expert team.

The work supports a fast-growing AI data services business that creates training data for major AI companies and foundation model labs. By keeping history data well-researched, balanced, contextualized, and clearly written, you will contribute to the quality of advanced AI systems.

There is no immediate project attached to this role. If you are a fit, you may be contacted first when relevant work becomes available, and you will gain access to future projects through the expert network.

What you will do

You will review history outputs and trainer/QA work against detailed rubrics, provide written feedback, surface recurring issues, support onboarding, maintain documentation, and help keep contributors aligned with the expected standards.

• Check history items for accuracy, chronology, source awareness, causation, context, regional and cultural nuance, interpretation quality, clarity, formatting, and instruction-following.
• Evaluate AI-generated explanations, timelines, comparisons, summaries, source-based responses, and reasoning for historical soundness and readability.
• Identify repeated quality problems and raise critical or recurring concerns to the appropriate people.
• Keep trainers and QAs informed about guideline updates, workflow changes, and expectations using remote communication tools.
• Respond quickly and clearly to questions on chronology, historical context, source interpretation, disputed readings, bias, and rubric use.
• Reach out to inactive contributors, encourage them to become active again, and track availability or follow-up needs.
• Create and update style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
• Run onboarding and training sessions to explain expectations, processes, standards, and review methods.
• Make sure review practices stay consistent across the team as projects change.
• Flag misleading, overly confident, biased, culturally insensitive, anachronistic, or weakly supported historical claims.
• Look for process gaps and suggest improvements that make the QA workflow more scalable and reliable.

What the role requires

This position calls for deep history knowledge, strong written English, very close attention to detail, and the ability to communicate structure and feedback clearly in a remote environment.

• A bachelor’s, master’s, or PhD in History, Classics, Area Studies, Archaeology, Political History, Cultural History, International Relations, Humanities, or a related discipline.
• At least 3 years of experience in historical research, teaching, writing, editing, academic review, museum or archival work, curriculum development, or a similar humanities-based role.
• A solid command of English for following instructions and giving precise written feedback.
• Strong understanding of historical method, chronology, primary and secondary sources, historiography, causation, continuity and change, regional context, and evidence-based interpretation.
• The ability to assess work against detailed rubrics and detect anachronisms, chronology mistakes, unsupported claims, oversimplification, biased framing, fabricated citations, or misleading causal logic.
• Preferred exposure to one or more historical specializations such as ancient, medieval, modern, world, military, intellectual, social, economic, colonial/postcolonial, or regional history.
• Experience leading or supporting remote teams of researchers, writers, reviewers, educators, annotators, or QA professionals is strongly preferred.
• Comfort using tools like Discord, Google Sheets, Google Docs, trackers, dashboards, and project management software.
• Strong organization skills for maintaining documentation, onboarding assets, FAQs, style guides, and calibration materials.
• Experience with AI training, data annotation, LLM evaluation, academic QA, fact-checking, or rubric-based review is a plus.

Selection process

The hiring process includes an AI interview, a domain-specific task, and an interview with a recruiter.

Additional information

This is an hourly remote contractor role. The source material indicates full-time remote employment, so the listing has been normalized as a full-time remote contract opportunity. No stipend or salary amount was stated.

No vacancy count, start date, application deadline, or work duration was provided.