Life & Environmental Sciences Team Lead
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 SuperAnnotate, 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.
Requirements: - 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. - Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback. - 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.