Remote Conservation Scientist: Guide AI in Environment

Alignerr Corp.; Cape Town; Remote; ZAR 344,000 - 689,000; Part time

Benefits offered by this job

Autonomy

Global collaboration

Flexible schedule

Job summary

Alignerr, fully remote, is seeking a Natural Resource Conservation Scientist to help train AI systems with environmental science, land management, and conservation decision-making. You will review questions and scenarios, evaluate AI content for ecological accuracy, and ensure recommendations reflect real-world conservation practices.

Ideal candidates have 3+ years of experience, strong knowledge of ecosystems, and the ability to provide structured, actionable feedback in a remote, asynchronous

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 applied recommendations with clarity.
• Self-motivated and reliable in a remote, asynchronous work environment.

Responsibilities

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

Job description

Alignerr, fully remote, is seeking a Natural Resource Conservation Scientist to help train AI systems with environmental science, land management, and conservation decision-making. You will review questions and scenarios, evaluate AI content for ecological accuracy, and ensure recommendations reflect real-world conservation practices.

Ideal candidates have 3+ years of experience, strong knowledge of ecosystems, and the ability to provide structured, actionable feedback in a remote, asynchronous