Remote AI Training Conservation Scientist

Alignerr Corp.; Melbourne; Remote; AUD 55,000 - 83,000; Part time

Job summary

Alignerr invites experienced conservation professionals to help train AI systems in environmental science, land management, and conservation decision-making. You will review datasets, assess AI outputs for accuracy, and provide actionable feedback to improve algorithms.

The role is remote, hourly-based, with flexible hours (10–40 hours per week). Suitable for researchers or consultants seeking meaningful, globally collaborative work that applies science to real-world land stewardship.

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.
• Comfortable reviewing structured written content and delivering detailed, actionable feedback.
• Self-motivated and reliable when working independently on remote, task-based projects.

Responsibilities

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

Skills

Experience in conservation

Ecosystems knowledge

Critical evaluation

Remote work

Education

Master's/PhD in Natural Resources or related field

Job description

Alignerr invites experienced conservation professionals to help train AI systems in environmental science, land management, and conservation decision-making. You will review datasets, assess AI outputs for accuracy, and provide actionable feedback to improve algorithms.

The role is remote, hourly-based, with flexible hours (10–40 hours per week). Suitable for researchers or consultants seeking meaningful, globally collaborative work that applies science to real-world land stewardship.