Natural Resource Conservation Scientist

Natural Resource Conservation Scientist (AI Training) About The Role

AI is rapidly expanding into environmental science — and it needs experts like you to get it right. We’re looking for experienced natural resource conservation scientists to evaluate and improve AI systems trained on conservation science, land management, and environmental decision-making.

Your expertise will directly shape how AI understands ecosystems, interprets land use data, and recommends conservation strategies — making a real‑world impact on how this technology serves scientists, policymakers, and communities.

• Organization:
  Alignerr (Powered by Labelbox)
• Type:
  Hourly / Task-based Contract
• Location:
  
  Fully Remote
• Commitment: 10–40 hours/week

• Review conservation science questions and scenarios used in AI training datasets
• Evaluate the scientific accuracy of AI-generated content related to land use, ecosystems, soil, water, and biodiversity
• Assess whether AI-generated recommendations reflect real‑world conservation practices and current scientific standards
• Identify errors, gaps, or misleading reasoning in AI outputs
• Provide clear, structured feedback to improve scientific reasoning and response quality
• Work independently and asynchronously — on a schedule that fits your life

• 3+ years of professional experience in natural resource conservation, environmental science, or a closely related field
• Strong working knowledge of ecosystems, land management principles, and conservation frameworks
• Able to critically evaluate scientific reasoning and applied conservation recommendations
• Comfortable reviewing structured written content and articulating detailed, constructive feedback
• Self‑motivated and reliable — you can manage your own time and meet deadlines without hand‑holding

• Master’s degree or PhD in Natural Resources, Environmental Science, Ecology, or a related discipline
• Hands‑on fieldwork or applied conservation project experience
  
  Familiarity with AI systems, content evaluation workflows, or scientific writing review

• Competitive hourly pay for specialized scientific expertise
• Fully remote and flexible — work from anywhere, on your own schedule
• Meaningful impact — your knowledge helps ensure AI gets environmental science right
• Cutting‑edge exposure — gain firsthand insight into how large language models are trained and evaluated
• Freelance autonomy — no micromanagement, no rigid hours, global collaboration
• Potential for ongoing work and contract extension based on performance