AI Trainer; Earth & Environmental Sciences

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
• Strong practical proficiency with Python.
• Deep expertise in at least one relevant earth or environmental-science specialization.
• Hands‑on experience using computational methods for geospatial analysis, modeling, simulation, forecasting, remote sensing, statistics, or reproducible research.

Relevant specializations may include geoscience, geology, geophysics, atmosphere and climate, meteorology, ocean and marine science, hydrology, remote sensing, environmental science, and sustainability.

• Peer-reviewed publications or preprints involving computational methods
• Public geospatial, climate, ocean, or environmental analysis code
• Google Scholar, ORCID, or equivalent research profiles
• Open‑source scientific software contributions
• Public datasets, models, notebooks, maps, or simulations
• Publicly documented forecasting, remote‑sensing, or environmental projects

• Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
• Experience reviewing other AI trainers' technical submissions.
• Experience with Num Py, Sci Py, pandas, xarray, Geo Pandas, GDAL, rasterio, NetCDF, or comparable tools.
• Experience validating computational results against observations, measurements, or established models.
• Availability to begin immediately.

Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.

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