In this hourly, remote contractor role, you will serve as a Sociology Quality Assurance Lead, overseeing quality, consistency, and trainer performance across sociology-focused AI training projects. Your responsibilities will include reviewing AI-generated sociology content, evaluating output quality against project guidelines, providing precise written feedback, and ensuring adherence to expected quality standards. You will assess work for sociological accuracy, theory application, research literacy, social context, methodological rigor, ethical awareness, bias sensitivity, clarity, formatting, instruction-following, and adherence to project-specific rubrics. Additionally, you will identify recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and assist in activating contributors who are not consistently engaged.

About the Company

We are a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs. Your sociology quality leadership will directly enhance the accuracy and ethical awareness of sociology training data used in premier AI models.

Responsibilities

• Quality monitoring: Spot-check sociology items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Sociology review: Evaluate AI-generated sociology explanations, theory applications, research summaries, social policy discussions, demographic interpretations, and reasoning for accuracy and nuance.
• Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and sociology-specific review standards.
• Question handling: Respond to trainer/QA questions clearly and promptly, especially around sociological concepts, social theory, research methods, inequality, culture, ethics, 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 sociology 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 sociology-specific review requirements.
• Quality alignment: Ensure all trainers and QAs apply sociology review guidelines consistently and understand updates as projects evolve.
• Bias and ethics review: Flag stereotyping, stigmatizing language, unsupported claims about social groups, weak causal reasoning, or ethically problematic social analysis.
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for sociology AI training projects.