History Quality Assurance Lead

Role overview

This is a remote, hourly contract position for a History Quality Assurance Lead. The role focuses on keeping history-related AI training work accurate, consistent, and aligned with project standards. You will review AI-produced history content as well as trainer and QA outputs, then give clear written feedback to maintain quality across the team.

The work sits within a fast-scaling AI data services organization that supplies training data to major AI companies and foundation model labs. Your contribution will directly support better AI outputs by making sure history content is well-grounded, contextual, balanced, clearly explained, and documented to client expectations.

There is no project ready to start immediately for this position. However, if you are a strong fit, you may be contacted first when relevant work becomes available and may also gain access to future assignments through the expert network.

What the role covers

You will assess history content for factual accuracy, sequence of events, use of sources, causal reasoning, context, regional and cultural nuance, interpretation quality, clarity, formatting, instruction adherence, and project-rubric compliance. The role also involves identifying repeated quality problems, communicating updates to trainers and QAs, supporting onboarding, keeping documentation current, and helping inactive contributors become active again.

Responsibilities

• Review sample history items, identify quality gaps, and share actionable feedback through direct messages while escalating serious or recurring concerns.
• Check AI-generated explanations, timelines, comparisons, summaries, source-based responses, and reasoning for historical accuracy, balance, clarity, and context.
• Keep trainers and QAs informed on Discord about guideline updates, workflow changes, quality expectations, and history-specific review standards.
• Answer questions from trainers and QAs promptly, especially on chronology, context, source interpretation, disputed viewpoints, bias, regional nuance, and rubric use.
• Contact inactive or unresponsive contributors, encourage reactivation, track follow-ups, and report availability issues where needed.
• Build and maintain project documentation such as style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding resources.
• Organize and lead onboarding or training calls to walk trainers and QAs through expectations, processes, rubrics, and review standards.
• Make sure all trainers and QAs apply the historical review guidelines consistently as project requirements evolve.
• Flag content that is misleading, overly confident, biased, culturally insensitive, anachronistic, or insufficiently sourced.
• Recommend process improvements and help develop scalable QA workflows for history-focused AI training projects.

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.
• Strong English communication skills for following instructions, collaborating with teams, and writing 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-related work.
• A solid understanding of historical methods, 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 anachronisms, chronology errors, unsupported claims, overgeneralization, biased framing, fabricated citations, or weak causal reasoning.
• Familiarity 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 leading or supporting remote teams of researchers, writers, reviewers, educators, annotators, or QAs is strongly preferred.
• Comfort using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems in a fast-paced remote setting.
• Strong organization and attention to detail, with the ability to manage style guides, FAQs, trackers, onboarding materials, calibration tasks, and documentation.
• Experience 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 role is structured as hourly contract work and is fully remote. While immediate project availability is not guaranteed, qualified candidates may be contacted first for future opportunities in the expert network.