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 — making a real-world impact on how this technology is used in environmental decision-making.

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

What You'll Do

- Review AI-generated forestry and land management scenarios for scientific accuracy and practical soundness
- Assess content related to forest health, land use, sustainability, and ecosystem management
- Identify errors, oversimplifications, or flawed reasoning in AI-generated recommendations
- Provide structured, expert feedback to improve how AI models reason through applied environmental problems
- 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
- Robust working knowledge of forest ecosystems, silviculture, and sustainable land-use practices
- Able to critically evaluate applied environmental decision-making scenarios
- Comfortable reading and reviewing written technical content
- Self-motivated and reliable — 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
- Fully remote and flexible — work on your own schedule
- Freelance perks: autonomy, variety, and global collaboration
- Contribute to meaningful work that ensures AI gets environmental science right
- Potential for ongoing work and contract extension

📌 Forestry and Land Management Scientist (AI Training) (Canada)
🏢 Alignerr
📍 Canada