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.

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.

Why Work with SME Careers?

• Be a part of a forward-thinking team and help us train AI to communicate more effectively.
• Shape the Future of AI

  Your work directly contributes to how AI systems learn and communicate.

• Your Schedule, Your Rules

  Set your own hours and work around your life.

• Weekly Pay

  Receive fast, reliable payments once work is approved.

• Community Rewards

  Get rewarded for referrals with ongoing bonus income.

• Quality monitoring: Spot-check life and environmental science items, identify quality issues, provide ongoing feedback through DMs, and escalate 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.

• Bachelor’s, Master’s, PhD, or equivalent professional experience in Biology, Environmental Science, Ecology, Conservation Biology, Earth Science, Public Health, Agriculture, Marine Science, Sustainability, Life Sciences, or a related field.
• 3+ years of experience in life science research, environmental science, teaching, fieldwork, lab work, conservation, sustainability, science communication, academic review, or related scientific workflows.
• Strong understanding of biology, ecology, ecosystems, biodiversity, evolution, genetics, physiology, environmental systems, climate change, pollution, conservation, sustainability, and scientific methods.
• Ability to evaluate life/environmental science content against detailed rubrics and identify issues such as incorrect biological claims, oversimplified ecological relationships, unsupported environmental claims, flawed causal reasoning, unsafe recommendations, or misleading data interpretation.
• Familiarity with tools or methods such as field sampling, lab methods, ecological surveys, environmental impact assessment, GIS, statistics, climate/environmental datasets, literature review, or scientific visualization is preferred.
• Experience leading or supporting remote teams of researchers, educators, reviewers, environmental specialists, 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, scientific QA, environmental content review, academic review, or rubric-based review is a strong plus.

Required

Work arrangement: Fully remote

Eligible countries: India