Forestry and Land Management Scientist (AI Training)

Phoenix

**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