Climate AI Training Data Scientist (Remote)

Alignerr Corp.; Boston (MA); Remote; USD 55,000 - 83,000; Part time

Benefits offered by this job

Fully remote

Freelance autonomy

Potential for ongoing work

Job summary

Alignerr is seeking Climate Data Specialists to design and evaluate real-world environmental problems that train AI models. This is a fully remote, hourly contract role (10–40 hours/week) for Masters- and PhD-level scientists looking to apply deep domain knowledge in climate, ecology, and sustainability.

You will collaborate asynchronously with AI researchers, use Python or R for data analysis, and ensure scientific rigor in model reasoning and environmental outputs.

Qualifications

• Master's or PhD in Environmental Science, Climate Science, Ecology, or a closely related field.
• Strong working knowledge of climate modeling, ecological systems, or sustainability science.
• Comfortable coding in Python or R for research or data analysis.

Responsibilities

• Design and develop advanced, real-world environmental science problems spanning climate modeling, ecology, sustainability, and related fields.
• Solve and critically review complex problem sets to ensure scientific accuracy, depth, and rigor.
• Apply domain expertise to help AI models reason more accurately about environmental systems.
• Collaborate asynchronously with AI researchers and fellow domain experts to refine and improve AI outputs.
• Ensure all deliverables meet high standards of scientific clarity and precision.

Skills

Independent work

Written communication

English fluency

Education

Master's degree in Environmental Science/Climate Science

PhD in a closely related field

Tools

Python

R

Job description

Alignerr is seeking Climate Data Specialists to design and evaluate real-world environmental problems that train AI models. This is a fully remote, hourly contract role (10–40 hours/week) for Masters- and PhD-level scientists looking to apply deep domain knowledge in climate, ecology, and sustainability.

You will collaborate asynchronously with AI researchers, use Python or R for data analysis, and ensure scientific rigor in model reasoning and environmental outputs.