Political Science QA Lead

Role overview

We are looking for a Political Science Quality Assurance Lead to oversee consistency, accuracy, and trainer performance across political science AI training initiatives. In this remote contractor position, you will review both AI-produced political science content and the work of trainers and QAs, verify quality against project guidelines, and deliver clear written feedback that helps contributors meet the required standards.

Your work will focus on checking conceptual accuracy, institutional detail, comparative context, political theory, policy reasoning, source awareness, bias sensitivity, clarity, formatting, instruction compliance, and alignment with project rubrics. You will also identify repeated quality problems, share updates with trainers and QAs, support onboarding, maintain project documentation, and help re-engage contributors who are inactive.

This is part of a fast-growing AI data services business that supports major AI companies and foundation model labs with training data. The role contributes directly to improving AI models by helping ensure political science data is balanced, accurate, well-contextualized, analytically sound, and consistent with client expectations.

Selection process

The hiring process includes an AI interview, a subject-matter task, and a recruiter interview.

Availability note

There is no active project available immediately for this position. If you are a strong fit, you may be contacted first when relevant opportunities become available, and you will also gain access to future assignments through the expert network.

Requirements

Applicants should hold a bachelor’s, master’s, or doctoral degree in Political Science, International Relations, Public Policy, Government, Political Theory, Security Studies, Law, Sociology, Economics, or a closely related social science discipline.

You should have at least 3 years of experience in political science research, teaching, policy analysis, public affairs, international relations, political writing, academic review, or a similar analytical role.

Strong command of English is needed for interpreting guidelines, coordinating with teams, and writing precise feedback.

Deep knowledge of political institutions, comparative politics, international relations, public policy, political theory, electoral systems, governance, state capacity, civil society, and political behavior is expected.

You must be able to assess content using detailed rubrics and detect issues such as oversimplification, ideological bias, unsupported claims, incorrect institutional descriptions, weak causal reasoning, and poor comparative analysis.

Experience with democracy and authoritarianism, political economy, public administration, constitutional systems, electoral politics, conflict studies, international organizations, policy evaluation, or survey research is preferred.

Prior experience leading or supporting remote teams of analysts, researchers, reviewers, educators, annotators, or QAs is strongly preferred.

Comfort with remote collaboration tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems is important.

You should be highly organized and detail-oriented, with the ability to keep style guides, FAQs, trackers, onboarding resources, calibration exercises, and documentation up to date.

Background in AI training, data annotation, LLM evaluation, policy QA, academic review, fact-checking, or rubric-based review is a strong advantage.

Responsibilities

• Monitor quality by reviewing samples of political science work, identifying errors, sending ongoing feedback, and escalating serious or repeated issues.
• Assess AI-generated political science explanations, comparative analyses, policy summaries, theory discussions, institutional descriptions, and reasoning for correctness and balance.
• Keep trainers and QAs informed through Discord about new instructions, workflow changes, quality expectations, and political science review standards.
• Answer trainer and QA questions promptly and clearly, especially on institutions, comparative politics, public policy, political theory, international relations, bias, and rubric interpretation.
• Reach out to inactive or unresponsive contributors, encourage them to resume work, track follow-ups, and flag availability concerns when necessary.
• Develop and maintain documentation such as style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
• Plan and run onboarding or training sessions that explain expectations, workflows, rubrics, quality standards, and political-science-specific review practices.
• Make sure trainers and QAs apply the review framework consistently and understand any updates as projects change.
• Identify misleading, one-sided, unsupported, outdated, politically charged, or poorly contextualized claims and flag them appropriately.
• Improve processes by finding recurring quality gaps, suggesting workflow refinements, and supporting scalable QA systems for political science AI training work.

Additional information

This is an hourly remote contractor role. The posting emphasizes that the work is part of an expert network and may be activated for future projects rather than an immediate assignment.

Terms and conditions

Selection is based on an AI interview, a domain-specific task, and a recruiter interview. Candidates should note that project availability is not immediate.