About The Role

Were looking for environmental management professionals to help shape how AI understands sustainability, land-use planning, and environmental decision-making. Your real-world expertise will directly influence the quality and accuracy of AI systems used by researchers, policymakers, and practitioners worldwide.

This is a fully remote, flexible contract role where your knowledge genuinely matters — youre not just reviewing content, youre helping AI get environmental science right.

• Organization: Alignerr (Powered by Labelbox)


• Type: Hourly / Task-based Contract


• Location: Remote


• Commitment: 10–40 hours/week

What Youll Do

• Review and evaluate AI-generated environmental management scenarios, analyses, and recommendations


• Assess the quality of AI reasoning related to sustainability frameworks, impact mitigation, and resource planning


• Identify gaps between theoretical approaches and real-world environmental practice


• Provide clear, structured feedback to improve the accuracy and applicability of AI outputs


• Work independently and asynchronously on your own schedule

Who You Are

• 3+ years of hands-on experience in environmental management, conservation,
  
  
  
  or a related field


• Strong working knowledge of sustainability frameworks, environmental planning, and impact assessment


• Able to critically evaluate written environmental analyses with a practiced eye


• Skilled at delivering detailed, structured, and constructive feedback


• Self-motivated and comfortable managing your own workload remotely

Nice to Have

• Masters degree in Environmental Management, Environmental Science, or a related discipline


• Experience with environmental policy, regulatory compliance, or permitting frameworks


• Familiarity with AI systems, content evaluation, or data annotation workflows

Why Join Us

• Work on cutting-edge AI projects with top research labs


• Fully remote and adaptable — work on your own schedule, anywhere


• Freelance perks: autonomy, variety, and global collaboration


• Contribute to meaningful work that improves how AI handles real environmental challenges


• Exposure to advanced large language models (LLMs) and how theyre trained


• Potential for ongoing work and contract extension