Earth Sciences Computational Workflow Expert

You will turn real Earth science workflows into self-contained terminal tasks that test how well AI agents handle scientific computing. The work covers environmental, climate, atmospheric, geospatial, geological, hydrological, oceanographic, and time-series data.

You will build reproducible environments, write expert solutions, define grading criteria, and check that scientific outputs are accurate and properly documented.

• Prepare and process climate, atmospheric, geological, hydrological, oceanographic, and geospatial datasets.
• Create solutions with Python, R, Bash, Julia, or another scientific programming language.
• Develop tasks involving numerical modeling, interpolation, forecasting, remote sensing, and environmental risk analysis.
• Define objective tests for data transformations, model behavior, and spatial or temporal accuracy.
• Validate coordinate systems, units, timestamps, missing-data handling, scientific assumptions, file formats, and metadata.
• Document data provenance, expected outputs, edge cases, limitations, and automated tests.

What It Pays and Takes

This is a remote contractor assignment scheduled for five weeks. The role requires advanced Earth science or closely related technical experience and independent work in Linux and terminal-based environments.

• $300 per approved task.
• At least 40 hours per week for five weeks, including four hours of overlap with Pacific Time.
• Open worldwide as a remote assignment.
• Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences or a closely related field.
• Strong knowledge of climate science, atmospheric science, geophysics, oceanography, geology, hydrology, remote sensing, environmental modeling, or Earth-system science.
• Strong programming ability in Python, R, Julia, Bash, or a comparable scientific language.
• Hands-on experience with scientific data processing, numerical modeling, geospatial analysis, environmental datasets, or time-series analysis.
• Understanding of scientific quality control, uncertainty, numerical accuracy, and reproducible workflows.

Helpful Background

Experience with common scientific and geospatial tools is useful, as is work involving reproducible software environments, automated testing, scientific benchmarking, or AI coding-agent evaluation.

• NumPy, pandas, SciPy, xarray, rasterio, GeoPandas, Cartopy, or GDAL.
• NetCDF, HDF5, GeoTIFF, shapefiles, or GRIB data formats.
• Docker, Conda, Git, CI/CD, automated testing, or high-performance computing environments.
• Publications or open-source contributions in Earth, environmental, geospatial, or computational sciences.

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 create and review examples that help artificial intelligence systems improve. Specialists are needed for technical work because accurate data, sound methods, and careful evaluation shape how these systems perform.