Environmental Management Scientist (AI Training)
About this role
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
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