Climate Data Specialist (AI Training)

About The Role

What if your years of environmental science training could directly shape how AI understands our planet's most complex systems? We're looking for Climate Data Specialists with advanced degrees to develop expert-level problems in climate modeling, ecology, and sustainability — problems that will train the next generation of AI models to reason more accurately about the natural world.

This is a fully remote, versatile contract role built for researchers and scientists who want to apply their expertise in a high-impact, cutting-edge context. Training and onboarding support is provided.

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

What You'll Do

Design, solve, and review advanced environmental science problems with real-world scientific relevance
Apply deep expertise in climate modeling, ecology, sustainability, or related disciplines to craft complex, rigorous problem statements
Evaluate AI-generated outputs for scientific accuracy, logical reasoning, and depth




Collaborate asynchronously with AI researchers and fellow domain experts to strengthen model reasoning
Ensure clarity, scientific rigor, and intellectual depth across all deliverables
Work independently on task-based assignments — fully on your own schedule

Who You Are

Holds a Master's or PhD in Environmental Science, Climate Science, Ecology, or a closely related field
Experienced with Python or R for research, data analysis, or modeling
Deep subject matter expertise in climate modeling, ecology, sustainability, or environmental systems
Exceptional written communicator — you can explain complex science clearly and precisely
Detail-oriented and methodical with a strong commitment to scientific accuracy
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 review, or AI evaluation workflows
Background in peer review,

📌 Climate Data Specialist (India)
🏢 Alignerr
📍 India