Natural Resource Conservation Scientist

Alignerr; Toronto; Remote; CAD 55,000 - 83,000; Part time

Benefits offered by this job

Fully remote

Flexible schedule

Potential for ongoing work

Job summary

Alignerr Corp. (Powered by Labelbox) is seeking a Natural Resource Conservation Scientist to help evaluate AI training data for environmental science and conservation decision-making.

This remote, contract role offers flexible hours and a chance to impact how AI understands land use, ecosystems, soil, water, and biodiversity. You will review questions and scenarios used to train AI, provide clear feedback, and work asynchronously on a 10-40 hour per week schedule.

Qualifications

• 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 frameworks.
• Able to critically evaluate scientific reasoning and applied recommendations with clarity.
• Comfortable reviewing structured written content and providing detailed, actionable feedback.
• Self-motivated and reliable when working independently on deadline-driven tasks.

Responsibilities

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

Skills

Ecosystems knowledge

Environmental science expertise

Critical evaluation

Scientific feedback

Independent work

Education

MS/PhD in Natural Resources

Tools

AI evaluation workflows

Job description

Natural Resource Conservation Scientist (AI Training)

About The Role

Your field expertise has real value beyond the field - and AI needs it now. We're looking for experienced natural resource conservation scientists to help evaluate and improve AI systems trained on environmental science, land management, and conservation decision-making.

This is a fully remote, flexible contract role where your scientific knowledge directly shapes how AI understands and communicates complex conservation topics. If you care about getting environmental science right, this is your opportunity to make an impact at the frontier of AI development.

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

What You'll Do

• Review conservation science questions and real-world scenarios used to train AI systems
• Evaluate the scientific accuracy of AI-generated content covering land use, ecosystems, soil, water, and biodiversity
• Assess whether AI recommendations reflect sound, real-world conservation practices
• Provide clear, structured feedback to improve scientific reasoning and AI outputs
• Work independently and asynchronously - on your schedule, at your 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 frameworks
• Able to critically evaluate scientific reasoning and applied recommendations with clarity
• Comfortable reviewing structured written content and providing detailed, actionable feedback
• Self-motivated and reliable when working independently on deadline-driven tasks

Nice to Have

• Master's degree or PhD in Natural Resources, Environmental Science, or a related discipline
• Hands-on fieldwork or applied conservation project experience
• Familiarity with AI systems, content evaluation workflows, or scientific writing

Why Join Us

• Meaningful work - your expertise helps ensure AI gets environmental science right
• Fully remote and flexible - work from anywhere, on a schedule that suits you
• Cutting-edge exposure - gain insight into how advanced AI language models are trained and evaluated
• Autonomy - task-based structure gives you control over your workload
• Global collaboration - connect with a network of subject matter experts across disciplines
• Potential for ongoing work - strong contributors are considered for contract extensions and new projects