Soil and Water Conservation Scientist (AI Training) About The Role We're looking for experienced soil and water conservation scientists to help evaluate and improve AI systems trained on environmental science content. Your expertise will directly shape how AI understands and communicates sustainable land and water management — making a real impact on one of the most important fields in environmental science.
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 soil and water conservation scenarios for scientific accuracy and practical soundness
Assess content covering erosion control, watershed management, soil health, and related topics
Identify flawed assumptions, outdated methods, or impractical recommendations in AI outputs
Provide structured, expert feedback to improve scientific rigor, clarity, and real-world applicability
Work independently and asynchronously on your own schedule
Who You Are
3+ years of hands-on experience in soil science, hydrology, conservation planning, or a closely related field
Strong working knowledge of soil processes, water systems, and conservation techniques
Able to critically evaluate applied environmental science reasoning and identify where AI falls short
Comfortable reviewing and responding to technical written content with precision and clarity
Self-motivated and reliable when working independently on task-based assignments
Nice to Have
Graduate degree in Soil Science, Hydrology, Environmental Science, or a related discipline
Experience with field assessments, conservation planning tools, or regulatory frameworks
Familiarity with AI systems or content evaluation workflows
Why Join Us
Work on cutting-edge AI projects with top research labs and help shape how AI handles real environmental science
Fully remote and flexible — work on your schedule, wherever you are
Freelance perks: autonomy, variety, and collaboration with experts across the globe
Contribute to meaningful work that improves AI accuracy in conservation and environmental stewardship