Remote Climate Data Specialist for AI Training

Alignerr Corp.; Sydney; Remote; AUD 55,000 - 124,000; Part time

Job summary

Alignerr is seeking Climate Data Specialists to shape AI training for climate and environmental science problems. This fully remote hourly contract role offers flexible 10–40 hours per week and requires advanced degrees, with training provided and no prior AI experience necessary.

You'll design and evaluate complex environmental science problems, leveraging Python or R, to improve AI model reasoning, while collaborating asynchronously with researchers and domain experts.

Qualifications

• Masters or PhD in Environmental Science, Climate Science, Ecology, or related field.
• Strong coding experience in Python or R for research or data analysis.
• Expertise in climate modeling, ecology, sustainability, or environmental systems.
• Clear scientific writing and attention to detail.
• Ability to work independently in asynchronous environments.
• Fluent in English and based in eligible regions.

Responsibilities

• Design, solve, and review advanced environmental science problems with real-world relevance and scientific rigor.
• Apply climate modeling, ecology, sustainability, or related disciplines to create complex problem statements.
• Evaluate AI-generated responses for scientific accuracy, depth, and clarity; collaborate asynchronously with researchers.
• Ensure all deliverables meet high standards of scientific integrity and communicative precision.

Skills

Python

R

Climate modeling

Ecology

Environmental data analysis

Education

Master's degree in Environmental Science / Climate Science / Ecology

PhD in Environmental Science / Climate Science (advantage)

Job description

Alignerr is seeking Climate Data Specialists to shape AI training for climate and environmental science problems. This fully remote hourly contract role offers flexible 10–40 hours per week and requires advanced degrees, with training provided and no prior AI experience necessary.

You'll design and evaluate complex environmental science problems, leveraging Python or R, to improve AI model reasoning, while collaborating asynchronously with researchers and domain experts.