Forestry and Land Management Scientist (AI Training)
Alignerr
Forestry and Land Management Scientist (AI Training) 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 decisions, and conservation strategies - making a real impact on how this technology serves the environmental science community.
- Organization
: Alignerr (Powered by Labelbox) - Type
: Hourly / Task-based Contract - Location
: Remote - Commitment
: 10-40 hours/week
- 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 management recommendations in AI outputs
- Provide clear, structured feedback to improve the applied reasoning of AI systems
- Work independently and asynchronously on your own schedule
- 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-use practices
- Ability to critically evaluate applied environmental decision-making scenarios
- Comfortable reviewing and assessing written technical content
- Detail-oriented, reliable, and self-motivated
- 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
- 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 improves how AI handles real-world environmental science
- Potential for ongoing work and contract extension
Vacancy posted 3 days ago
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