About The Role We're looking for experienced environmental management professionals to help evaluate and improve AI systems trained on sustainability, land-use planning, and environmental decision-making. Your real-world expertise will directly shape how AI understands and communicates complex environmental topics — making a tangible impact on how these tools serve practitioners, policymakers, and the planet.
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
Flag inaccuracies, oversimplifications, or misleading guidance in AI outputs
Provide clear, structured feedback that improves the accuracy and applicability of AI content
Work independently and asynchronously on task-based assignments that fit your 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 identify where they fall short
Comfortable providing detailed, well-organized written feedback
Self-motivated, reliable, and comfortable working independently
Nice to Have
Master's degree in Environmental Management, Environmental Science, or a related discipline
Experience with environmental policy, regulatory compliance, or EIA frameworks
Familiarity with AI tools or content evaluation workflows
Why Join Us
Work on cutting-edge AI projects with top research labs and make a real difference in how AI handles environmental topics
Fully remote and flexible — work on your own schedule, wherever you are
Freelance perks: autonomy, variety, and global collaboration
Gain firsthand exposure to advanced large language models (LLMs) and how they're trained