Natural Resource 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

Natural Resource Conservation Scientist (AI Training)

About The Role

Are you a natural resource or environmental scientist who wants to do something truly different with your expertise? We're looking for experienced conservation professionals to help evaluate and improve AI systems being trained on environmental science, land management, and conservation decision-making.

Your scientific knowledge will directly shape how AI understands ecosystems, interprets conservation data, and recommends land management strategies — making a real-world difference in how this technology serves the environment and the people who protect it.

• 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, 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
  

Who You Are

• 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
  

Nice to Have

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

Why Join Us

• Meaningful impact — your expertise helps ensure AI gets environmental science right
• Fully remote and flexible — work when and where it suits you
• Cutting-edge work — gain firsthand exposure to how advanced AI models are trained and improved
• Autonomy — task-based structure means you control your workload
• Global collaboration — work alongside experts from around the world
• Potential for ongoing work — strong contributors are considered for contract extensions and future projects