Remote Conservation Scientist for AI Training & Evaluation

Alignerr Corp.; Miami (FL); Remote; USD 55,000 - 110,000; Part time

Job summary

Alignerr, a labelbox-powered organization, is seeking an experienced natural resource conservation scientist to help train AI on conservation, land management, and environmental decision-making. This is a fully remote contract role with flexible hours and 10–40 hours per week.

You will review datasets, evaluate AI-generated content for ecological accuracy, and provide structured feedback to improve model reasoning. Work independently and asynchronously from anywhere in the world.

Qualifications

• 3+ years of professional experience in natural resource conservation, land management, or environmental science.
• Strong working knowledge of ecosystems, conservation principles, and applied land management.
• Able to critically evaluate scientific reasoning and identify errors or gaps in applied recommendations.
• Self-motivated and reliable in a remote, asynchronous work environment.

Responsibilities

• Review conservation science questions, scenarios, and case studies used in AI training datasets.
• Evaluate the scientific accuracy of AI-generated content covering ecosystems, land use, soil health, water systems, and biodiversity.
• Assess whether AI recommendations reflect real-world conservation practices and sound scientific reasoning.
• Provide clear, structured feedback to improve the quality and reliability of AI outputs.
• Work independently and asynchronously on your own schedule.

Skills

NR conservation experience

Ecosystems knowledge

Critical evaluation

Remote work capability

Education

Master's or PhD in Natural Resources or related field

Job description

Alignerr, a labelbox-powered organization, is seeking an experienced natural resource conservation scientist to help train AI on conservation, land management, and environmental decision-making. This is a fully remote contract role with flexible hours and 10–40 hours per week.

You will review datasets, evaluate AI-generated content for ecological accuracy, and provide structured feedback to improve model reasoning. Work independently and asynchronously from anywhere in the world.