Natural Resource Conservation Scientist

Your field expertise has real power beyond the landscape — it can shape how AI understands and communicates environmental science. We’re looking for experienced natural resource conservation scientists to help evaluate and improve AI systems trained on conservation, land management, and environmental decision-making.

This is a fully remote, flexible contract role where your scientific knowledge directly influences how the next generation of AI models reasons about the natural world.

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

• Review conservation science questions, scenarios, and case studies used in AI training datasets
• Evaluate the scientific accuracy of AI-generated content covering ecosystems, land use, soil health, water systems, and biodiversity
• Assess whether AI recommendations reflect real-world conservation practices and sound scientific reasoning
• Provide clear, structured feedback to improve the quality and reliability of AI outputs
• Work independently and asynchronously on your own schedule

• 3+ years of professional experience in natural resource conservation, land management, or environmental science
• Strong working knowledge of ecosystems, conservation principles, and applied land management
• Able to critically evaluate scientific reasoning and identify errors or gaps in applied recommendations
• Comfortable reviewing structured written content and delivering detailed, actionable feedback
• Self-motivated and reliable in a remote, asynchronous work environment

• Master's degree or PhD in Natural Resources, Environmental Science, Ecology, or a related field
• Hands‑on fieldwork or applied conservation project experience
• Familiarity with AI systems, content evaluation, or annotation workflows

• Meaningful impact:
  Your expertise helps ensure AI gets environmental science right — at scale
• Cutting‑edge work:
  Gain hands‑on exposure to advanced large language models (LLMs) and how they’re trained
• Full flexibility:
  Work remotely on your own schedule, as much or as little as your availability allows
• Freelance autonomy:
  No micromanagement — you own your workflow
• Global collaboration:
  Join a community of subject‑matter experts from around the world
• Ongoing opportunity:
  Strong contributors are considered for contract extensions and future projects