Remote Climate Data Specialist for AI Training

Alignerr Corp.; New York (NY); On-site; USD 14,000 - 55,000; Part time

Benefits offered by this job

Fully remote

Flexible hours

Freelance autonomy

Contract extension potential

Job summary

Alignerr is seeking environmental science experts to help build and train cutting-edge AI models. This fully remote, hourly contract role welcomes Masters or PhD-level scientists who want to apply climate science, ecology, or sustainability expertise to real-world AI challenges.

Offered as a flexible, task-based engagement with 10–40 hours per week, the position supports ongoing collaboration with AI researchers while delivering rigorous, scientifically sound problem sets and outputs.

Qualifications

• MS or PhD in Environmental Science or related field.
• Strong expertise in climate modeling, ecology, sustainability, or data analysis.
• Proficient in Python or R for research and analysis.
• Excellent written communicator with clear scientific explanations.
• Fluent in English; based in the U.S., Canada, U.K., Australia, or NZ.

Responsibilities

• Design, solve, and review advanced environmental science problems with real-world relevance.
• Apply expertise to create complex problem sets for AI training.
• Collaborate asynchronously with AI researchers and domain experts.
• Evaluate quality of AI outputs in your field.
• Ensure accuracy and integrity across deliverables.

Skills

Python or R

Climate modeling

Ecology

Sustainability

Environmental data analysis

Education

MS in Environmental Science

PhD in Climate Science

Job description

Alignerr is seeking environmental science experts to help build and train cutting-edge AI models. This fully remote, hourly contract role welcomes Masters or PhD-level scientists who want to apply climate science, ecology, or sustainability expertise to real-world AI challenges.

Offered as a flexible, task-based engagement with 10–40 hours per week, the position supports ongoing collaboration with AI researchers while delivering rigorous, scientifically sound problem sets and outputs.