Environmental Management Scientist (AI Training)

About The Role

We're looking for experienced environmental management professionals to help shape the next generation of AI systems. Your real-world expertise in sustainability, land-use planning, and environmental decision-making will directly influence how AI understands and communicates complex environmental topics — making a meaningful impact on how these systems serve researchers, policymakers, and practitioners worldwide.

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

What You'll 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 environmental models and real-world 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 and spot where reasoning falls short
• Comfortable providing detailed, structured written feedback
• Self-motivated and reliable when working independently

Nice to Have

• Master's 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 annotation workflows

Why Join Us

• Work on cutting-edge AI projects with top research labs
• Fully remote and flexible — work on your own schedule
• Freelance perks: autonomy, variety, and global collaboration
• Contribute to meaningful work that improves how AI handles real-world environmental challenges
• Potential for ongoing work and contract extension