The Work
You will create realistic, terminal-based tasks that test whether AI systems can solve Earth sciences and scientific computing problems. Tasks should require meaningful reasoning, code execution, simulations, troubleshooting, and outputs that can be checked objectively.
You will turn research workflows into reproducible challenges across climate science, atmospheric processes, geophysics, oceanography, geology, hydrology, remote sensing, and environmental modeling.
- Prepare and process climate, weather, atmospheric, oceanographic, geological, hydrological, satellite, and other environmental datasets.
- Develop tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, and environmental risk.
- Build terminal environments with scientific libraries, command-line tools, and the required dependencies.
- Create reference solutions with Python, R, Bash, Julia, or relevant scientific software.
- Write automated tests and evaluation criteria for scientific correctness, numerical accuracy, spatial and temporal consistency, and reproducibility.
- Validate coordinate systems, units, timestamps, missing-data handling, uncertainty, and scientific assumptions.
- Document data sources, workflows, expected outputs, edge cases, and scientific limitations.
What It Pays And Takes
The assignment is remote and open worldwide. It is a contractor role expected to last four weeks, with a part-time classification and a time requirement of 20 or more hours per week.
English fluency is required for the role. The work calls for advanced Earth sciences knowledge and strong scientific programming ability.
- Pay: Not provided in the listing.
- Required background: Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences, Climate Science, Atmospheric Science, Geophysics, Oceanography, Geology, Hydrology, Environmental Science, or a related field.
- Strong programming ability in Python, R, Bash, Julia, or another relevant scientific language.
- Hands-on experience with Linux and terminal-based computational environments.
- Practical experience in scientific data processing, numerical modeling, geospatial analysis, climate modeling, remote sensing, or environmental data analysis.
- Strong understanding of spatial datasets, time-series analysis, numerical accuracy, uncertainty, data quality control, and reproducible workflows.
- Ability to independently develop, debug, validate, and clearly document computational solutions.
- Helpful tools and formats include NumPy, pandas, SciPy, xarray, rasterio, GeoPandas, Cartopy, GDAL, NetCDF, HDF5, GeoTIFF, shapefiles, GRIB, Docker, Conda, Git, CI/CD, and automated testing frameworks.
- Additional useful experience includes high-performance computing, Earth-system models, environmental forecasting, geophysical simulations, research software engineering, scientific benchmarks, automated grading, AI coding agents, or scientific quality control.
How It Works
Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is a free platform where people find AI training and data-labeling projects, build a profile, and apply in minutes.
About AI Training Work
AI training work is the human work behind systems that learn from examples. Contributors prepare data, write or review code, and evaluate model outputs so AI systems can perform technical tasks more accurately; advanced subject knowledge matters when tasks require scientific judgment and reliable validation.