Environmental Management Scientist (AI Training)

About the Role

We're looking for experienced 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 tackling some of the most important topics on the planet.

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 structured, detailed feedback to improve the clarity, accuracy, and applicability of AI outputs
• Work independently and asynchronously to complete task-based assignments on your own schedule

Who You Are

• 3+ years of hands-on experience in environmental management, conservation, or a closely related field
• Strong working knowledge of sustainability frameworks, environmental planning principles, and impact assessment methodologies
• Able to critically evaluate written environmental analyses and identify weaknesses or inaccuracies
• Skilled at providing clear, structured written feedback
• Self-motivated and comfortable working independently in a remote setting

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 workflows, or data annotation

Why Join Us

• Work on cutting-edge AI projects with top research labs and make a real impact on how AI handles environmental topics
• Fully remote and flexible — work from anywhere, on your own schedule
• Freelance perks: autonomy, variety, and collaboration with a global network of subject-matter experts
• Gain exposure to advanced large language models (LLMs) and how they are trained and evaluated
• Potential for ongoing work and contract extension based on performance

Organization

Organization: Alignerr (Powered by Labelbox)

Type

Type: Hourly / Task-Based Contract

Location

Location: Remote

Commitment

Commitment: 10–40 hours/week