Natural Resource Conservation Scientist

Alignerr; Mumbai; Remote; INR 3,283,000 - 6,566,000; Part time

Job summary

Alignerr (Powered by Labelbox) seeks a Natural Resource Conservation Scientist to help train AI on environmental science, conservation policy, and land stewardship. You will review ecosystem case studies and scenarios used in AI training datasets and provide expert feedback on scientific accuracy.

This is a fully remote, hourly, task-based contract with 10–40 hours per week. Candidates should have 3+ years in natural resource conservation or environmental science and be comfortable working

Qualifications

• 3+ years of professional experience in natural resource conservation or environmental science
• Strong working knowledge of ecosystems, land management principles, and conservation decision-making
• Able to critically evaluate scientific reasoning and applied recommendations with confidence
• Comfortable reviewing structured written content and providing detailed feedback
• Self-motivated and reliable when working independently on remote, asynchronous tasks

Responsibilities

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

Skills

Natural resource conservation

Environmental science

Remote work

Critical evaluation

Feedback delivery

Education

Master's degree or PhD preferred

Tools

AI platforms

Data annotation platforms

Job description

Natural Resource Conservation Scientist (AI Training)

About The Role

What if your expertise in ecosystems, land management, and conservation could help shape how AI understands the natural world? We're looking for experienced natural resource conservation scientists to join a cutting-edge AI training project — evaluating and improving how AI systems reason about environmental science, conservation policy, and land stewardship.

Your real-world knowledge will directly influence the quality and accuracy of AI used by researchers, policymakers, and practitioners around the globe.

• Organization: Alignerr (Powered by Labelbox)
• Type: Hourly / Task-based Contract
• Location: Fully Remote
• Commitment: 10–40 hours/week

What You'll Do

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

Who You Are

• 3+ years of professional experience in natural resource conservation, environmental science, or a closely related field
• Strong working knowledge of ecosystems, land management principles, and conservation decision‑making
• Able to critically evaluate scientific reasoning and applied recommendations with confidence
• Comfortable reviewing structured written content and providing detailed, actionable feedback
• Self‑motivated and reliable when working independently on remote, asynchronous tasks

Nice to Have

• Master's degree or PhD in Natural Resources, Environmental Science, Ecology, or a related discipline
• Hands‑on fieldwork or applied conservation project experience
• Familiarity with AI systems, content evaluation workflows, or data annotation platforms

Why Join Us

• Work on the frontier of AI — contribute to projects with real impact on how AI understands our natural world
• Fully remote and flexible — work from anywhere, on your own schedule
• Autonomy and variety — no two projects are exactly alike; your expertise guides the work
• Global collaboration — connect with a diverse network of subject matter experts worldwide
• Potential for ongoing work — strong contributors are considered for contract extensions and future projects
• Meaningful contribution — help ensure AI gets environmental science right, at a time when it matters most