About this role
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.
Responsibilities
- 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.
- Native fluency in Punjabi
Skills
- Climate Systems
- LLM Evaluation
- Environmental Science
- Biology
- Ecology
- AI Training
- Sustainability
- Trainer Feedback
- Life Sciences
- Scientific Review
Additional skills
- Ecology
- Environmental Science
- Life Sciences Research
- Biodiversity
- Conservation Biology
- Sustainability
- Climate Change
- Environmental Impact Assessment (EIA)
- Ecosystems
- Evolutionary Biology
- Genetics
- Physiology
- Pollution Monitoring
- Field Sampling
- Ecological Surveys
- Laboratory Techniques
- GIS (Geographic Information Systems)
- Remote Sensing
- Spatial Analysis
- Statistics
- R Programming
- Python
- Data Analysis
- Scientific Visualization
- Climate & Environmental Datasets
- Literature Review
- Scientific Writing
- Peer Review
- Science Communication
- Team Leadership
- Project Management
