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 practices. Your domain expertise will directly influence the accuracy and reliability of AI systems used by researchers, practitioners, and decision-makers worldwide.
This is a fully remote, flexible contract role — work on your own schedule while contributing to cutting-edge AI development.
- 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 soundness of AI-generated content related to forest health, land use, and sustainability
- Identify factual errors, oversimplifications, or flawed management recommendations
- Provide clear, structured feedback to improve AI reasoning on applied environmental topics
- Work independently and asynchronously to complete task-based assignments 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 management practices
- Able to critically evaluate applied environmental decision-making scenarios
- Comfortable reading and reviewing technical written content with precision
- Self-motivated and reliable — you deliver quality work without close supervision
- 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, content evaluation, or data annotation workflows
Why Join Us
- Work on cutting-edge AI projects with top research labs
- Fully remote and flexible — work when and where it suits you
- Freelance perks: autonomy, variety, and global collaboration
- Make a meaningful impact by ensuring AI gets environmental science right
- Potential for ongoing work and contract extension
