Political Science Quality Assurance Lead; QAL

Pay: up to $70/hour

In this hourly, remote contractor role, you will work as a Political Science Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across political science AI training projects. You will review AI-generated political science 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 conceptual accuracy, institutional knowledge, comparative context, political theory accuracy, policy reasoning, source awareness, bias sensitivity, 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 political science 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 political science quality leadership will directly help improve the world’s premier AI models by ensuring that political science training data is accurate, balanced, well-contextualized, analytically sound, 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 political science items, identify quality issues, provide ongoing feedback through DMs, and escape recurring or critical issues.
• Political science review:
  Evaluate AI-generated political science explanations, comparative analyses, policy summaries, political theory discussions, institutional descriptions, and reasoning for accuracy and balance.
• Trainer and QA communication:
  Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and political-science-specific review standards.
• Question handling:
  Respond to trainer/QA questions clearly and promptly, especially around institutions, comparative politics, public policy, political theory, international relations, bias, 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 political science 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 political-science-specific review requirements.
• Quality alignment:
  Ensure all trainers and QAs apply political science review guidelines consistently and understand updates as projects evolve.
• Bias and risk review:
  Flag misleading, ideologically one-sided, unsupported, outdated, politically inflammatory, or poorly contextualized claims.
• Process improvement:
  Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for political…