AI Trainer (Earth & Environmental Sciences)

What You'll Do6

• Design realistic computational earth-science problems that test advanced AI models.
• Write task instructions, set up reproducible environments, and provide reference solutions and evaluation criteria.
• Review model-produced code, geospatial analyses, simulations, forecasts, and technical outputs.
• Spot errors in coordinate systems, spatial or temporal data handling, model assumptions, uncertainty, numerical methods, or reproducibility.
• Create objective standards for what counts as a correct solution.
• Evaluate model performance on scientific and technical tasks.

Requirements5

• Paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
• Strong practical proficiency in Python.
• Deep expertise in at least one earth or environmental-science specialization such as geoscience, geology, geophysics, atmosphere and climate, meteorology, ocean and marine science, hydrology, remote sensing, environmental science, or sustainability.
• Hands-on experience with computational methods for geospatial analysis, modeling, simulation, forecasting, remote sensing, statistics, or reproducible research.
• Public evidence of skill, such as peer-reviewed publications or preprints with computational methods, public geospatial or climate analysis code, Google Scholar or ORCID profiles, open-source scientific software contributions, public datasets, models, notebooks, maps, or simulations, or documented forecasting, remote-sensing, or environmental projects.

Preferred Qualifications5

• Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
• Experience reviewing other AI trainers' technical submissions.
• Familiarity with NumPy, SciPy, pandas, xarray, GeoPandas, GDAL, rasterio, NetCDF, or similar tools.
• Experience validating computational results against observations, measurements, or established models.
• Availability to begin immediately.

Who Should Apply

Computational earth or environmental scientists who have already done paid AI-training or model-evaluation work and can show public evidence of their computational projects. You should be proficient in Python and have deep expertise in a specialization like climate, hydrology, or remote sensing.

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