Turing
TuringVerified listing
Remote

Domain Expert- Politics

Remote
Posted August 26, 2026
contract
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Listing checked August 26, 2026 · pay as published by Turing

Overview

Turing seeks a Politics Domain Expert to evaluate and improve Large Language Models with a focus on political science, governance, elections, public policy, and international relations. The role involves designing challenging prompts that test factual accuracy, reasoning, completeness, and nuance in AI-generated responses. You will build benchmark datasets and adversarial test cases to expose hallucinations, logical inconsistencies, outdated information, and edge cases. Work happens remotely on a contractor basis for 8 weeks at 40 hours per week with a 4-hour PST overlap.

What You'll Do7

  • 1Design advanced prompts spanning political science, governance, elections, public policy, and international relations.
  • 2Assess AI outputs for factual accuracy, reasoning quality, completeness, and nuance.
  • 3Identify hallucinations, logical inconsistencies, outdated information, and edge cases in model responses.
  • 4Build benchmark datasets and adversarial test cases to probe model weaknesses.
  • 5Provide evidence-based feedback with reliable references to support evaluations.
  • 6Work with AI researchers to refine and improve model performance.
  • 7Maintain high annotation quality and document evaluation processes.

Requirements8

  • 1A Master's degree or higher in any field, with preference for Political Science, Public Policy, International Relations, Government, Public Administration, Law, Economics, Journalism, or a related politics discipline.
  • 2Strong knowledge of political systems, governance, elections, public policy, international relations, political institutions, comparative politics, and current global political developments.
  • 33+ years of professional experience in public policy, government, political research, political consulting, academia, journalism, international affairs, think tanks, or a related area.
  • 4Excellent written English, research, and analytical skills.
  • 5Strong attention to detail and ability to identify factual inconsistencies and assess reasoning across political and public policy topics.
  • 6Preferred: experience with LLMs, generative AI, prompt engineering, or AI evaluation.
  • 7Preferred: published research, industry recognition, or teaching experience.
  • 8Preferred: ability to work independently and provide objective, evidence-backed reviews.

Who Should Apply

The ideal candidate holds an advanced degree in a politics-related field and has at least 3 years of experience in policy, government, research, academia, journalism, or international affairs. They combine deep domain expertise with sharp written English and the ability to spot factual inconsistencies or weak reasoning in AI-generated political content. Candidates who cannot commit to 40 hours per week with a 4-hour PST overlap should not apply, since the 8-week contractor assignment demands that schedule. Those without any exposure to LLMs, prompt engineering, or AI evaluation may struggle with the assessment, even though this experience is only preferred. Common rejection reasons include vague or unsupported feedback in the evaluation task, and a failure to demonstrate rigorous, evidence-based assessment of political topics.

Pay and demand for AI Training roles

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Typical pay

$70/hour

This role

Pay not disclosed

Most AI Training roles pay $40–$98 per hour.

Based on 1,035 similar roles that publish pay · 302 publish only a top rate; those count at the rate they gave

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Location

TypeRemote
LocationRemote
This is a remote position

Compensation

Undisclosed by Turing

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Required Skills

political sciencegovernanceelectionspublic policyinternational relationspolitical systemspolitical institutionscomparative politicsprompt engineeringlarge language modelsgenerative aiai evaluationresearchanalytical writingbusiness analysis

Application Tip

During the assessment, show how you construct prompts that test specific political edge cases, and cite authoritative sources when explaining why a model response is wrong. Quantify your domain experience by naming the policy areas or regions you have worked on, and make your evaluation criteria explicit so reviewers can see your reasoning quality.

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