About the Role
We're looking for experienced forestry and land management scientists to help evaluate and improve AI systems trained on sustainable forestry and land-use practices. Your field expertise will directly shape how AI understands forest ecosystems, management strategies, and environmental decision-making — making a real impact on how these critical topics are represented in next-generation AI tools.
- 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 practices
- Identify factual errors, oversimplifications, or flawed reasoning in management recommendations
- Provide clear, structured feedback to improve the quality of AI-generated applied reasoning
- Work independently and asynchronously on your own schedule
Who You Are
- 3+ years of hands-on experience in forestry, land management, or a related field
- Strong understanding of forest ecosystems, silviculture, and land management principles
- Able to critically evaluate applied environmental and resource management decision-making
- Comfortable reviewing and assessing written technical content with precision
- Self-motivated, reliable, and able to meet deadlines 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 compliance
- Familiarity with AI systems or content evaluation workflows
Why Join Us
- Work on cutting-edge AI projects with top research labs shaping the future of AI
- Fully remote and flexible — work on your own schedule, from anywhere
- Freelance perks: autonomy, variety, and meaningful work with real-world impact
- Contribute to ensuring AI gets environmental science right — for researchers, practitioners, and policymakers who rely on it
- Potential for ongoing work and contract extension