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# Remote Domain Expert - Politics

- **Company:** Cornerstone Barricades
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $83,200.0 - $104,000.0
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Prompt Engineering, Generative AI
- **Published:** August 28, 2026
- **Apply:** https://turing.betterteam.com/remote-domain-expert-politics-3/apply

## About the Role

* Master's degree or higher in any field; degrees in Political Science, Public Policy, International Relations, Government, Public Administration, Law, Economics, Journalism, or other politics-related disciplines are preferred.
 * Strong knowledge of politics, including political systems, governance, elections, public policy, international relations, political institutions, comparative politics, and current global political developments.
 * 3+ years of relevant professional experience, preferably in public policy, government, political research, political consulting, academia, journalism, international affairs, think tanks, or a related field.
 * Excellent written English, research, and analytical skills.
 * Strong attention to detail and ability to evaluate political information accurately, identify factual inconsistencies, and assess reasoning across a wide range of political and public policy topics., * Experience with LLMs, Generative AI, prompt engineering, or AI evaluation.
 * Published research, industry recognition, or teaching experience is a plus.
 * Ability to work independently and provide objective, evidence-backed reviews.

## Description

* Create advanced prompts covering political science, governance, elections, public policy and international relations.
 * Evaluate AI-generated responses for factual accuracy, reasoning quality, completeness, and nuance.
 * Identify hallucinations, logical inconsistencies, outdated information, and edge cases.
 * Develop benchmark datasets and adversarial test cases.
 * Provide evidence-based feedback with reliable references.
 * Collaborate with AI researchers to improve model performance.
 * Maintain high annotation quality and documentation.


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