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Environmental Management Scientist (AI Training)
Job description
Environmental Management Scientist (AI Training)
About the Role
We'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 planet's most pressing challenges.
This is a fully remote, flexible contract role — work on your own schedule while contributing to cutting-edge AI development.
- 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 and analyses
- Assess the quality of AI reasoning related to sustainability, impact mitigation, and resource planning
- Identify gaps between theoretical frameworks and real-world environmental practice
- Provide clear, structured feedback to improve AI accuracy and applicability
- Work independently and asynchronously to meet project milestones
Who You Are
- 3+ years of hands-on experience in environmental management, conservation, or a related field
- Strong command of sustainability frameworks, environmental planning, and impact assessment
- Able to critically evaluate written environmental analyses with precision
- Comfortable delivering detailed, structured feedback in written form
- Self-motivated and reliable in a remote, asynchronous work environment
Nice to Have
- Master's degree in Environmental Management, Environmental Science, or a related discipline
- Experience with environmental policy, regulation, or compliance frameworks
- Familiarity with AI systems, content evaluation, or annotation workflows
Why Join Us
- Work on cutting-edge AI projects with top research labs
- Fully remote and flexible — design your own schedule
- Freelance perks: autonomy, variety, and global collaboration
- Contribute to meaningful work that helps AI better understand environmental challenges
- Exposure to advanced large language models (LLMs) and how they are trained
- Potential for ongoing work and contract extension
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