History Quality Assurance Lead (QAL)

Job Summary

As a History Quality Assurance Lead (QAL), you will oversee quality, consistency, and contributor performance across history-focused AI training projects.

This role involves reviewing AI-generated historical content, evaluating trainer and QA outputs, maintaining quality standards, and ensuring training data aligns with project requirements and client expectations.

Your work will directly contribute to improving advanced AI systems by ensuring history training content is accurate, contextualized, balanced, well-sourced, and clearly explained.

Key Responsibilities

Quality Monitoring

• Spot-check history training tasks and QA outputs
• Identify recurring quality issues and historical inaccuracies
• Provide actionable written feedback and escalate critical concerns

Historical Content Review

• Review and evaluate historical explanations, timelines, comparative analyses, historical summaries, source-based responses, and reasoning and interpretations
• Assess work for historical accuracy, chronological correctness, source awareness, historical context, causation analysis, regional and cultural nuance, clarity, and instruction adherence

Trainer & QA Communication

• Communicate guidelines, workflow changes, history review standards, quality expectations, and project requirements

Contributor Support

• Answer questions involving historical chronology, historical methods, source interpretation, historiography, disputed interpretations, cultural context, bias evaluation, and rubric interpretation

Activation Management

• Follow up with inactive contributors, track engagement and participation, report contributor availability concerns

Documentation & Onboarding

• Create and maintain style guides, documentation, FAQs, examples, calibration tasks, and onboarding materials
• Conduct onboarding and training sessions for contributors

Bias & Quality Review

• Identify and flag anachronisms, incorrect chronology, unsupported claims, oversimplification, biased framing, fabricated citations, misleading causal explanations, and culturally insensitive content

Process Improvement

• Improve QA workflows and review processes
• Identify recurring quality gaps and implement corrective actions

Required Qualifications

• Bachelor's, Master's, or PhD in History, Classics, Area Studies, Archaeology, Political History, Cultural History, International Relations, Humanities, or related disciplines (or equivalent professional experience)
• Strong English communication skills
• Minimum 3 years of experience in historical research, teaching, academic writing, editing, academic review, museum work, archival work, or curriculum development
• Strong understanding of historical methods, chronology, primary and secondary sources, historiography, historical causation, continuity and change, evidence-based interpretation, and regional context
• Ability to identify anachronisms, unsupported claims, incorrect chronology, fabricated citations, historical bias, oversimplified interpretations, and misleading explanations

Preferred Qualifications

• Specialization in Ancient, Medieval, Modern, World, Military, Intellectual, Social, Economic, Colonial/Postcolonial, or Regional History
• Experience leading researchers, writers, reviewers, educators, QA teams, or remote contributors
• Experience with AI training, data annotation, LLM evaluation, academic QA, and rubric-based review

Why Join

• Help improve leading AI systems through historical content quality assurance
• Flexible remote schedule
• Weekly payments
• Referral rewards and community incentives
• Access to future opportunities through SME Careers' expert network

Selection Process

1. AI Interview
2. Domain-Specific Assessment
3. Recruiter Interview

Important Note

There is currently no active project for this role. Qualified candidates will be added to the expert network and contacted when relevant opportunities become available.