Forestry and Land Management Scientist (AI Training)

Forestry and Land Management Scientist (AI Training)

About the Role

We're looking for experienced forestry and land management scientists to help shape the next generation of AI. Your field expertise will directly influence how AI systems understand, reason about, and communicate sustainable forestry and land-use practices — work that matters for the planet and for the future of environmental AI.

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

What You'll Do

• Review forestry and land management scenarios used in AI training datasets
• Assess the accuracy of AI-generated content related to forest health, land use, and sustainability
• Identify errors, oversimplifications, or misleading recommendations in AI outputs
• Provide clear, structured feedback to improve AI reasoning on applied environmental topics
• Work independently and asynchronously on your own schedule

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 sustainable land-use practices
• Able to critically evaluate applied environmental decision-making and management recommendations
• Comfortable reading and reviewing technical written content with precision
• Self-motivated and reliable when working independently on remote projects

Nice to Have

• Degree in Forestry, Natural Resources, Environmental Science, or a related field
• 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 leading AI organizations
• Fully remote and flexible — work on your own schedule, from anywhere
• Freelance perks: autonomy, variety, and global collaboration
• Use your domain expertise in a meaningful new way that shapes real-world AI applications
• Potential for ongoing work and contract extension