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
