Alignerr
Environmental Management Scientist (AI Training)
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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
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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
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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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