About The RoleWe're looking for 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 challenges of our time.This is a fully remote, flexible contract role — work on your own schedule and apply your knowledge in an exciting new frontier.- Organization: Alignerr (Powered by Labelbox)- Type: Hourly Contract- Location: Remote- Commitment: 10–40 hours/weekWhat 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 clear, structured feedback to improve the accuracy and applicability of AI outputs- Work independently and asynchronously to complete task-based assignments on your own scheduleWho You Are- 3+ years of hands-on experience in environmental management, conservation,
or a closely related field- Strong working knowledge of sustainability frameworks and environmental planning principles- Able to critically evaluate written environmental analyses for accuracy, completeness, and practical relevance- Comfortable providing detailed, structured written feedback- Self-motivated and reliable when working independently- No prior AI experience requiredNice to Have- Master's degree in Environmental Management, Environmental Science, or a related discipline- Experience with environmental policy, regulatory frameworks, or compliance- Familiarity with AI systems or content evaluation workflowsWhy Join Us- Work on cutting-edge AI projects with top research labs- Fully remote and versátil — work on your own schedule, from anywhere- Freelance perks: autonomy, variety, and global collaboration- Contribute to meaningful work that improves how AI addresses real environmental challenges- Exposure to advanced large language models (LLMs) and how they're trained- Potential for ongoing work and contract extension#J-18808-Ljbffr