Remote Forestry & Land Management Scientist for AI Training

Alignerr Corp.; Atlanta (GA); Remote; USD 55,000 - 83,000; Full time

Job summary

Alignerr is seeking experienced forestry and land management scientists to shape how AI understands sustainable forest practices. This remote, flexible hourly contract lets you work on your own schedule while contributing to cutting-edge AI development.

You will review forestry datasets, assess AI-generated content for accuracy, and provide clear feedback to improve AI reasoning on environmental topics. Collaboration is asynchronous with potential for ongoing work.

Qualifications

• 3+ years hands-on experience in forestry, land management, or related field.
• Strong knowledge of forest ecosystems, silviculture, and land management practices.
• Ability to critically evaluate applied environmental decision-making and spot inaccuracies.
• Comfort reviewing and assessing written technical content.
• Self-motivated and reliable when working independently.

Responsibilities

• 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.
• Identify factual errors, oversimplifications, or flawed management recommendations.
• Provide clear, structured feedback to improve AI reasoning on environmental topics.
• Work independently and asynchronously on task-based assignments.

Skills

Forestry experience

Land management

Critical evaluation

Written content review

Independent work

Education

Degree in Forestry

Degree in Natural Resources

Degree in Environmental Science

Tools

AI tools

Content evaluation workflows

Job description

Alignerr is seeking experienced forestry and land management scientists to shape how AI understands sustainable forest practices. This remote, flexible hourly contract lets you work on your own schedule while contributing to cutting-edge AI development.

You will review forestry datasets, assess AI-generated content for accuracy, and provide clear feedback to improve AI reasoning on environmental topics. Collaboration is asynchronous with potential for ongoing work.