Ocean, Atmosphere and Earth Observation Scientist (English)

You will create research-level problems in oceanography, atmospheric science, geoscience, or a related field. Your work will use practical knowledge of raw field, instrument, and survey data from cruises, deployments, and field campaigns.

You will work independently on flexible, task-based assignments. Each contribution must be technically accurate, clearly written, and well documented.

• Write research-level problems for training and evaluating AI models.
• Use raw instrument, survey, and field data to develop sound scientific tasks.
• Process and quality-check your own data with Python or a similar programming language.
• Explain technical scientific work clearly in writing.
• Manage your time across flexible assignments.

What it pays and takes

This is remote contractor work with no fixed weekly schedule.

• Pay: $50 per hour.
• Work arrangement: Remote, flexible contractor work with no fixed weekly schedule.
• Location: Open to candidates in the listed countries.
• Language: Professional fluency in English.
• Required: Hands-on experience with raw field and instrument data.
• Required: Working knowledge of Python for data processing and quality checks.
• Required: Experience with cruises, field deployments, or field campaigns.
• Required: Ability to work independently as an individual contractor.
• Visa sponsorship is not provided.
• Helpful: A master's or PhD in oceanography, atmospheric science, geoscience, or a related field.
• Helpful: Experience as a postdoctoral researcher or research staff member at an oceanographic, meteorological, or geological institute.
• Helpful: Familiarity with calibration, instrument drift, quality-control pipelines, or real-world survey data.
• Helpful: Experience with data annotation, data labeling, AI evaluation, or machine-learning evaluation.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work uses human expertise to prepare examples and evaluate outputs so AI systems can improve. Scientific specialists are needed when tasks require accurate interpretation of real-world data and subject knowledge.