Climate Data Specialist (AI Training)

About The Role

What if your deep expertise in climate modeling, ecology, or environmental science could directly shape how AI reasons about one of the most critical challenges facing our planet? We're looking for Climate Data Specialists — Masters and PhD-level scientists — to develop advanced, real-world problems that train cutting-edge AI models to think like experts.

This is a fully remote, versátil contract role built for researchers and scientists who want to apply their domain knowledge in a high-impact, forward-looking way. No prior AI background needed — just rigorous scientific thinking and a command of your field.

Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week

What You'll Do

Design and develop advanced, real-world environmental science problems spanning climate modeling, ecology, sustainability, and related disciplines
Solve and review complex problem sets to ensure scientific accuracy, rigor, and depth




Apply your domain expertise to challenge and improve AI model reasoning across environmental topics
Collaborate asynchronously with AI researchers and fellow domain experts to refine outputs
Ensure all deliverables meet high standards of clarity, scientific integrity, and intellectual depth

Who You Are

Hold a Master's or PhD in Environmental Science, Climate Science, Ecology, or a closely related field
Deeply knowledgeable in climate modeling, ecology, sustainability, or adjacent areas
Proficient in Python or R — whether from research, academic projects, or professional work
A precise, clear communicator who takes scientific accuracy seriously
Self-directed and comfortable working asynchronously in a remote environment
Fluent in English and based in the U.S., Canada, U.K., Australia, or New Zealand

Nice to Have

Prior experience with data annotation, data quality assessment, or AI evaluation workflows
Background in interdisciplinary environmental research or applied c

📌 Climate Data Specialist (España)
🏢 Alignerr
📍 España