History Quality Assurance Lead; QAL

Pay: up to $50/hour

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

You will assess work for historical accuracy, chronology, source awareness, causation, context, regional and cultural nuance, interpretation quality, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong history expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of Super Annotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your history quality leadership will directly help improve the world’s premier AI models by ensuring that history training data is accurate, contextualized, balanced, well-explained, well-documented, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important:

There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

• Quality monitoring:
  Spot-check history items, identify quality issues, provide ongoing feedback through DMs, and escalation recurring or critical issues.
• Historical review:
  Evaluate AI-generated history explanations, timelines, comparisons, summaries, source-based answers, and reasoning for accuracy, context, balance, and clarity.
• Trainer and QA communication:
  Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and history-specific review standards.
• Question handling:
  Respond to trainer/QA questions clearly and promptly, especially around chronology, historical context, source interpretation, disputed interpretations, bias, regional nuance, 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 history 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 history-specific review requirements.
• Quality alignment:
  Ensure all trainers and QAs apply historical-review guidelines consistently and understand updates as projects evolve.
• Risk and bias review:
  Flag misleading, overconfident, biased, culturally insensitive, anachronistic, or poorly sourced historical claims.
• Process improvement:
  Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for history AI training projects.

• Bachelor’s, Master’s, or PhD degree in History, Classics, Area Studies, Archaeology, Political History, Cultural History,…