Forestry and Land Management Scientist (AI Training)
Alignerr · Remote · AI model evaluation
Forestry and Land Management Scientist (AI Training) is an open role at Alignerr, available remotely, with pay listed as $30–$55/hr.
Applications open on Alignerr’s own site. How we make money.
At a glance
- Pay
- $30–$55/hrStated by Alignerr
- Location
- Remote
- Work type
- AI model evaluation
- Field
- Science / STEM
- Listed
- Since Oct 4, last confirmed Oct 22
How the pay compares
Higher than 20% of the 117 distinct hourly rates advertised for Science / STEM roles right now. Hourly USD, top of each advertised range, one count per distinct rate. Same method as the Pay Index.
From Alignerr’s listing
“Forestry and Land Management Scientist (AI Training) About the Role We're looking for experienced forestry and land management scientists to help shape the next generation of AI systems.”
Show the full listing
Forestry and Land Management Scientist (AI Training) About the Role We're looking for experienced forestry and land management scientists to help shape the next generation of AI systems. Your field expertise will directly influence how AI understands, reasons about, and communicates sustainable forestry and land-use practices — work that has real-world implications for how these tools are used by professionals and policymakers alike. Organization : Alignerr Type : Hourly Contract Location : Remote Commitment : Flexible, asynchronous What You'll Do Review forestry and land management scenarios used in AI training datasets Evaluate the accuracy of AI-generated content related to forest health, land use, and sustainability Identify errors, oversimplifications, or misleading recommendations in AI outputs Provide clear, structured feedback to improve the quality of applied environmental reasoning 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 Strong working knowledge of forest ecosystems, silviculture, and land management practices Able to critically evaluate applied environmental decision-making and spot technical inaccuracies Comfortable reviewing and assessing written technical content Detail-oriented, self-motivated, and reliable 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 leading research labs Fully remote and flexible — complete work on your own schedule Freelance perks: autonomy, variety, and global collaboration Apply your domain expertise to meaningful work that shapes how AI understands the natural world Potential for ongoing work and contract extension
About Alignerr: part of Labelbox. See the full profile →
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Frequently asked questions
How do I apply for the Forestry and Land Management Scientist (AI Training) role at Alignerr?
Applications go through Alignerr's own portal - HumanSourcer links to the listing and never collects applications or handles hiring. Alignerr's access model is "Open / self-serve" (Open roles / applications). This listing was first seen here on October 4, 2026 and was still live at the last check.
Is the Forestry and Land Management Scientist (AI Training) role at Alignerr remote?
Yes - Alignerr lists this role as remote.
What does the Forestry and Land Management Scientist (AI Training) role at Alignerr pay?
Alignerr lists this role's pay as $30–$55/hr, quoted from the listing rather than estimated.
What kind of work is Forestry and Land Management Scientist (AI Training)?
AI model evaluation. AI training, evaluation, coding, writing and specialist review
Is Alignerr a legitimate AI-training platform?
Labelbox expert network - and that relationship is rated "Confirmed" here because it can be checked against labelbox.com rather than taken on Alignerr's word. Confidence describes how well the ownership is evidenced. It is not a rating of how the platform treats the people who work for it, which no public source covers reliably.
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