About this role
In this hourly, remote contractor role, you will work as a Social Sciences & Education Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across social science, education, and learning-focused AI training projects. You will review AI-generated social science/education 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, research literacy, educational appropriateness, social context, methodology quality, ethical 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 social science and/or education 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 SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your social sciences and education quality leadership will directly help improve the world’s premier AI models by ensuring that social science and education training data is accurate, nuanced, research-informed, ethically aware, and aligned with client expectations.
Requirements
- Bachelor’s, Master’s, PhD, teaching credential, or equivalent professional experience in Education, Sociology, Psychology, Anthropology, Political Science, Social Work, Public Policy, Educational Psychology, Curriculum Studies, or a related field.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
- 3+ years of experience in teaching, curriculum development, social science research, educational content review, instructional design, academic writing, social policy, student assessment, or related review workflows.
- Strong understanding of social science concepts, education theory, pedagogy, learning outcomes, assessment design, research methods, ethics, bias, culture, social institutions, inequality, and evidence-based reasoning.
- Ability to evaluate social science/education content against detailed rubrics and identify issues such as unsupported generalizations, stereotyping, weak causal claims, poor instructional design, biased framing, flawed methodology, or age-inappropriate educational content.
- Familiarity with areas such as classroom learning, assessment, lesson planning, social research, qualitative/quantitative methods, educational equity, child development, learning science, or curriculum standards is preferred.
- Experience leading or supporting remote teams of educators, researchers, curriculum writers, reviewers, annotators, trainers, or QAs is strongly preferred.
- Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
- Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.
- Experience with AI training, data annotation, LLM evaluation, social science QA, educational QA, curriculum review, or rubric-based review is a strong plus.
Responsibilities
- Quality monitoring: Spot-check social science and education items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Social science and education review: Evaluate AI-generated explanations, lesson content, social science summaries, research-methods content, educational activities, assessment items, learning guidance, and social 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 social science/education-specific review standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around education concepts, learning objectives, research methods, social context, bias, student appropriateness, 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 social sciences/education 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 social science/education-specific requirements.
- Quality alignment: Ensure all trainers and QAs apply social science and education review guidelines consistently and understand updates as projects evolve.
- Bias and ethics review: Flag stereotyping, stigmatizing language, unsupported claims about groups, weak causal reasoning, poor educational scaffolding, or ethically problematic content.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for social science and education AI training projects.
Skills
- Research Methods
- LLM Evaluation
- Sociology
- Curriculum Review
- Pedagogy
- AI Training
- Social Sciences
- Trainer Feedback
- Psychology
- Education
Additional skills
- Team Leadership
- Remote Team Management
- Curriculum Development
- Instructional Design
- Educational Assessment
- Assessment Design
- Lesson Planning
- Learning Outcomes
- Pedagogy
- Education Theory
- Learning Science
- Educational Psychology
- Child Development
- Curriculum Standards
- Content Review
- Rubric Design
- Rubric-Based Evaluation
- Quality Assurance (QA)
- Academic Writing
- Research Methods
- Qualitative Research
- Quantitative Research
- Survey Design
- Data Analysis
- Evidence-Based Reasoning
- Bias Detection
- Research Ethics
- Educational Equity
- Data Annotation
- LLM Evaluation
