Environmental Management Scientist (AI Training)

About The Role

We're looking for environmental management professionals to help shape how AI understands sustainability, conservation, and environmental decision-making. Your real-world expertise will directly influence how cutting-edge AI systems reason about land-use planning, impact mitigation, and resource management — work that matters well beyond the screen.

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 outputs and real-world environmental practice Provide clear, structured feedback to improve the accuracy and applicability of AI responses Work independently and asynchronously to complete task-based assignments on your own schedule

Who You Are

3+ years of experience in environmental management, conservation, or a closely related field Strong working knowledge of sustainability frameworks, environmental planning, and impact assessment Able to critically evaluate written environmental analyses with a practiced eye Comfortable providing detailed, structured written feedback Self-motivated and reliable — you can manage your time and meet deadlines 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 systems, content evaluation, or data annotation workflows

Why Join Us

Work on meaningful AI projects — your environmental expertise directly improves how AI handles real-world sustainability challenges Fully remote and flexible — work on your own schedule, wherever you are Exposure to cutting-edge AI — gain rare insight into how large language models are trained and evaluated Autonomy and variety — no two days look the same; engage with diverse environmental scenarios and topics Potential for ongoing work — strong contributors are considered for contract extensions and future projects Global collaboration — connect with a network of subject-matter experts across disciplines and regions