School Science; Elementary) Team Lead

Pay: up to $10/hour

In this hourly, remote contractor role, you will work as a School Science (Elementary) Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across elementary-level science education AI training projects. You will review AI-generated school science 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 scientific accuracy, grade-level appropriateness, conceptual clarity, child-safe explanations, curriculum alignment, age-appropriate language, reasoning quality, 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 elementary science knowledge, educational judgment, strong English communication skills, excellent attention to detail, and the ability to manage quality workflows across remote education-focused teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of Super Annotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your elementary science quality leadership will directly help improve the world’s premier AI models by ensuring that school science training data is accurate, age-appropriate, engaging, safe, and clearly explained for young learners.

• Quality monitoring:
  Spot-check elementary science items, identify quality issues, provide ongoing feedback through DMs, and
  ** escalate
  * * recurring or critical issues.
• Science education review:
  Evaluate AI-generated science explanations, worksheets, activities, short answers, lesson-style content, examples, quizzes, and student-facing explanations for accuracy and grade-level fit.
• Trainer and QA communication:
  Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and elementary science review standards.
• Question handling:
  Respond to trainer/QA questions clearly and promptly, especially around grade-level appropriateness, scientific concepts, safe activities, student misconceptions, 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 elementary science 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 school-science-specific requirements.
• Quality alignment:
  Ensure all trainers and QAs apply elementary science review guidelines consistently and understand updates as projects evolve.
• Safety review:
  Flag unsafe experiments, misleading health/environment claims, overly advanced explanations, incorrect science facts, or age-inappropriate content.
• Process improvement:
  Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for elementary science AI training projects.
• Native fluency in Punjabi

• Bachelor’s, Master’s, teaching credential, or equivalent professional experience in Elementary Education, Science Education, Biology, Chemistry, Physics, Earth Science, Environmental Science, STEM Education, or a related field.
• Strong grasp of the English language to follow…