About the role

Turing seeks Earth Sciences experts to develop realistic, terminal-based scientific tasks for Terminal Bench Science. Create computational workflows involving environmental data, climate systems, atmospheric processes, geophysics, oceanography, geology and related fields.

Design tasks requiring AI agents to inspect scientific datasets, process geospatial or time-series data, run models, troubleshoot pipelines and generate objectively verifiable scientific outputs.

Scope of Work

• Translate authentic Earth-science workflows into self-contained terminal benchmark tasks.
• Prepare geospatial, climate, atmospheric, geological, hydrological or oceanographic datasets.
• Build reproducible computational environments with appropriate scientific libraries and command-line tools.
• Create expert solutions using Python, R, Bash, Julia or domain-specific software.
• Develop tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing or environmental risk analysis.
• Define objective grading criteria for scientific outputs, model behavior, data transformations and spatial or temporal accuracy.
• Validate coordinate systems, units, timestamps, missing-data handling and scientific assumptions.
• Create automated tests for numerical tolerances, file formats, metadata and reproducibility.
• Debug geospatial projections, large datasets, dependencies, performance and numerical stability.
• Document input data provenance, expected outputs, edge cases and limitations.

What you’ll bring

• Ph.D., postdoctoral experience or equivalent advanced technical experience in Earth Sciences or a closely related field.
• Strong expertise in climate science, atmospheric science, geophysics, oceanography, geology, hydrology, remote sensing, environmental modeling or Earth-system science.
• Strong programming skills in Python, R, Julia, Bash or another scientific programming language.
• Hands-on experience with scientific data processing, numerical modeling, geospatial analysis, environmental datasets or time-series analysis.
• Comfortable working independently in Linux/terminal-based environments.
• Ability to build, debug and validate reproducible scientific computational workflows.
• Strong understanding of scientific quality control, spatial/temporal data, uncertainty and numerical accuracy.
• Available at least 6 hours per day and 40 hours per week, with 4 hours of overlap with PST.

Preferred Qualifications

• Experience with NumPy, pandas, SciPy, xarray, rasterio, GeoPandas, Cartopy, GDAL or similar scientific/geospatial tools.
• Experience with NetCDF, HDF5, GeoTIFF, shapefiles or GRIB.
• Experience with climate, weather, satellite, seismic, oceanographic, geological or hydrological datasets.
• Familiarity with Docker, Conda, Git, CI/CD, automated testing or HPC environments.
• Research software engineering, scientific benchmarking or automated grader development experience.
• Experience evaluating AI coding/terminal agents or developing AI tasks and evaluations.
• Publications or open-source contributions in Earth, environmental, geospatial or computational sciences.

Benefits

Not provided in the source listing.

Schedule and availability

At least 6 hours per day and a minimum of 40 hours per week, including 4 hours of overlap with PST.

Contract & Payment Terms

• Contractor assignment with no medical benefits or paid leave.
• Expected contract duration: 5 weeks.
• The supplied posting describes the expected start as next week; confirm the current start date with Turing.
• Payment is per approved task at $300 USD.

Where you can work

The supplied posting does not specify work location or eligible countries. Confirm these with Turing.

Country eligibility has not been provided. Confirm with the employer.

Remote does not always mean work from any country. Always check the employer’s location and working-hour requirements.