About The Role We're looking for experienced forestry and land management scientists to help shape how AI understands sustainable forestry, forest ecosystems, and land-use decision-making. Your field expertise will directly influence the quality and accuracy of next-generation AI systems — making a real impact on how these tools support environmental professionals worldwide.

  • Organization: Alignerr (Powered by Labelbox)
  • Type: Hourly Contract
  • Location: Remote
  • Commitment: 10–40 hours/week What You'll Do
  • Review and evaluate AI-generated forestry and land management scenarios for scientific accuracy
  • Assess content related to forest health, land use planning, and sustainability practices
  • Identify errors, oversimplifications, or flawed reasoning in management recommendations
  • Provide clear, structured feedback that helps AI systems reason more accurately about real-world environmental decisions
  • Work independently and asynchronously on a schedule that fits your life Who You Are
  • 3+ years of hands-on experience in forestry, land management, or a closely related field
  • Strong working knowledge of forest ecosystems, silviculture, or sustainable land-use practices
  • Able to critically evaluate applied environmental decision-making and spot technical inaccuracies
  • Comfortable reviewing and responding to written technical content
  • Self-motivated and reliable when working independently Nice to Have
  • Degree in Forestry, Natural Resources, Environmental Science, or a related discipline
  • Experience with land-use planning, conservation programs, or regulatory compliance
  • Familiarity with AI systems or content evaluation workflows Why Join Us
  • Work on cutting-edge AI projects with top research labs
  • Fully remote and flexible — work on your schedule, wherever you are
  • Contribute to meaningful work that improves how AI handles real environmental science
  • Freelance perks: autonomy, variety, and global collaboration
  • Potential for ongoing work and contract extension

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