Forestry and Land Management Scientist (AI Training)

About The Role

We're looking for experienced forestry and land management scientists to help shape how AI understands and reasons about forest ecosystems, sustainable land use, and environmental decision-making. Your field expertise will directly influence the quality and reliability of next-generation AI systems — making a real-world impact from wherever you work.

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

What You'll Do

• Review and evaluate forestry and land management scenarios used in AI training datasets
• Assess the accuracy and scientific validity of content related to forest health, land use planning, and sustainability practices
• Identify errors, oversimplifications, or misleading recommendations in AI-generated outputs
• Provide clear, structured feedback that improves AI reasoning on applied environmental topics
• Work independently and asynchronously on your own schedule — no meetings required
  
  

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, and land-use management practices
• Ability to critically evaluate applied environmental decision-making scenarios
• Comfortable reviewing and annotating written technical content
• Self-motivated and reliable when working independently
• No prior AI experience required
  
  

Nice to Have

• Degree in Forestry, Natural Resources, Environmental Science, or a related discipline
• Experience with land-use planning, conservation programs, or regulatory frameworks
• Familiarity with AI systems or content evaluation workflows
  
  

Why Join Us

• Work on cutting-edge AI projects with top research labs and technology teams
• Fully remote and flexible — work when and where it suits you
• Freelance perks: autonomy, variety, and global collaboration
• Use your specialized expertise in a new and impactful way
• Contribute to AI systems that get environmental science right
• Potential for ongoing work and contract extension