Climate AI Researcher — Remote Contract
Alignerr Corp.
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Benefits offered by this job
Job summary
Alignerr is seeking a Climate Data Specialist (AI Training) for a fully remote hourly contract. You will design, solve, and review advanced environmental science problems with real-world relevance, applying climate modelling, ecology, and sustainability to craft rigorous prompts for AI training.
Holders of a Master’s or PhD in Environmental Science or related fields with Python or R proficiency will collaborate asynchronously with AI researchers to improve AI reasoning and evaluation of
Qualifications
- Master's or PhD in Environmental Science, Climate Science, Ecology, or related field.
- Fluent in Python or R and used in research or projects.
- Deep expertise in climate modelling, ecology, or sustainability science.
- Excellent written communication and ability to translate complex concepts clearly.
- Detail-oriented with rigorous standards.
Responsibilities
- Design, solve, and review advanced environmental science problems with genuine real-world relevance
- Apply expertise in climate modeling, ecology, sustainability to craft complex problem statements
- Collaborate asynchronously with AI researchers and domain experts to enhance AI reasoning on scientific challenges
- Evaluate AI-generated scientific content for accuracy, depth, and methodological soundness
- Ensure scientific rigor, clarity, and intellectual depth across all deliverables
- Work independently on task-based assignments — fully on your own schedule
Skills
Education
Job description
Alignerr is seeking a Climate Data Specialist (AI Training) for a fully remote hourly contract. You will design, solve, and review advanced environmental science problems with real-world relevance, applying climate modelling, ecology, and sustainability to craft rigorous prompts for AI training.
Holders of a Master’s or PhD in Environmental Science or related fields with Python or R proficiency will collaborate asynchronously with AI researchers to improve AI reasoning and evaluation of
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