Environmental Sciences Team Lead

Pay: up to $20/hour

In this hourly, remote contractor role, you will work as a Life & Environmental Sciences Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across biology, ecology, environmental science, and life science AI training projects. You will review AI-generated life/environmental 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, biological reasoning, ecological context, environmental systems thinking, terminology quality, data interpretation, safety awareness, 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 life/environmental science expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote science-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 life and environmental sciences quality leadership will directly help improve the world’s premier AI models by ensuring that science training data is accurate, evidence-aware, environmentally contextualized, clearly explained, and aligned with client expectations.

• Quality monitoring:
  Spot-check life and environmental science items, identify quality issues, provide ongoing feedback through DMs, and escape recurring or critical issues.
• Scientific review:
  Evaluate AI-generated biology, ecology, environmental science, sustainability, conservation, climate, and life science explanations for accuracy, clarity, and scientific rigor.
• Trainer and QA communication:
  Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and life/environmental-science-specific review standards.
• Question handling:
  Respond to trainer/QA questions clearly and promptly, especially around biological concepts, ecosystems, environmental systems, data interpretation, sustainability claims, safety, 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 life/environmental sciences 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 scientific review requirements.
• Quality alignment:
  Ensure all trainers and QAs apply life and environmental science guidelines consistently and understand updates as projects evolve.
• Risk review:
  Flag misleading environmental claims, unsupported health/ecology statements, unsafe experiment or field recommendations, flawed data interpretation, or overconfident scientific claims.
• Process improvement:
  Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for life and environmental science AI training projects.
• Native fluency in Punjabi

• Bachelor’s, Master’s, PhD, or equivalent professional experience in Biology, Environmental Science, Ecology,…