History Quality Assurance Lead

Role overview

This is a remote, hourly contractor position for a History Quality Assurance Lead. The role focuses on maintaining quality, consistency, and reviewer/trainer performance across AI training work centered on history. You will evaluate AI-generated historical content and QA output, compare it with project rules, and deliver clear written guidance so contributors meet the required standards.

The company is an AI data services provider supporting training-data work for major AI companies and foundation-model labs. In this position, your historical expertise will help improve AI systems by making sure the content is accurate, well-contextualized, balanced, clearly explained, and aligned with client expectations.

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

What the role involves

You will assess history-focused outputs for factual accuracy, sequence of events, use of sources, cause-and-effect reasoning, historical context, regional and cultural nuance, interpretation quality, clarity, formatting, and compliance with project rubrics. The job also includes spotting repeated quality problems, keeping contributors informed about updates, supporting onboarding, maintaining documentation, and helping re-engage people who are not working regularly.

Responsibilities

• Review sampled history items, identify defects, and provide continuous feedback through direct messages while escalating repeated or serious issues.
• Check AI-generated history answers, timelines, comparisons, summaries, source-based responses, and reasoning for correctness, balance, context, and readability.
• Share project updates with trainers and QA contributors through Discord, including guideline changes, workflow changes, quality expectations, and review standards.
• Answer trainer and QA questions clearly and quickly, especially on chronology, historical interpretation, source usage, disputed views, bias, regional nuance, and rubric application.
• Reach out to inactive contributors, encourage them to re-engage, follow up on activity, and flag availability concerns when needed.
• Build and keep project materials current, such as style guides, trackers, FAQs, examples, quality notes, honeypots, calibration tasks, and onboarding documents.
• Organize and run onboarding or training calls to explain expectations, workflows, rubrics, quality targets, and history-specific review standards.
• Make sure trainers and QAs apply the historical review framework consistently as project needs change.
• Identify misleading, overconfident, biased, culturally insensitive, anachronistic, or weakly sourced claims and flag them for review.
• Suggest process improvements and help create scalable QA workflows for history-related AI training tasks.

Requirements

• A bachelor’s, master’s, or PhD in History, Classics, Area Studies, Archaeology, Political History, Cultural History, International Relations, Humanities, or a closely related discipline.
• Excellent written English skills for following instructions, coordinating with teams, and giving precise feedback.
• At least 3 years of experience in historical research, teaching, writing, editing, academic review, museum or archival work, curriculum development, or similar humanities-based work.
• Solid command of historical method, chronology, primary and secondary sources, historiography, causation, continuity and change, regional context, and evidence-based interpretation.
• Ability to judge content against detailed rubrics and detect problems such as anachronisms, incorrect chronology, unsupported claims, oversimplification, biased framing, fabricated citations, or misleading causal explanations.
• Experience with one or more history specializations such as ancient, medieval, modern, world, military, intellectual, social, economic, colonial/postcolonial, or regional history is preferred.
• Prior experience guiding or supporting distributed teams of researchers, writers, reviewers, educators, annotators, or QAs is strongly preferred.
• Comfort working in fast-moving remote setups with tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management platforms.
• Strong organizational skills and attention to detail, with the ability to maintain style guides, FAQs, trackers, onboarding materials, calibration tasks, and documentation.
• Background in AI training, data annotation, LLM evaluation, academic QA, fact-checking, or rubric-based review is a strong advantage.

Selection process

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

Additional information

This is a contract role with hourly work and remote delivery. The opportunity is based in Germany. No immediate project is available right now, but qualified candidates may be contacted first when suitable projects arise.