Scientific Computing / Research Engineering Expert — Earth Sciences

About this opportunity

Turing is seeking Earth Sciences experts to develop realistic, terminal-based scientific tasks for Terminal Bench Science. The role involves creating computational workflows using environmental and scientific datasets, including climate, atmospheric, geospatial, geological, hydrological, oceanographic, and remote-sensing data. Experts will design tasks that require AI agents to inspect datasets, process geospatial and time-series information, run scientific models, troubleshoot computational pipelines, and generate objectively verifiable scientific outputs.

What the listing describes

• ✓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 relevant 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 issues involving geospatial projections, large datasets, dependencies, performance, and numerical stability
• ✓Document input data provenance, expected outputs, edge cases, and limitations
• ✓Develop realistic scientific workflows that can be objectively evaluated by automated systems
• ✓Support the creation and validation of computational benchmarks for AI agents

Requirements shown on the source

• ✓Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences or a closely related field
• ✓Strong expertise in at least one area such as 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 and terminal-based environments
• ✓Ability to build, debug, and validate reproducible scientific computational workflows
• ✓Strong understanding of scientific quality control, spatial and temporal data, uncertainty, and numerical accuracy
• ✓Ability to work with scientific datasets and computational workflows in a reproducible manner
• ✓Ability to troubleshoot issues involving projections, large datasets, dependencies, performance, and numerical stability
• ✓Ability to document scientific assumptions, data provenance, expected outputs, edge cases, and limitations
• ✓Ability to work independently in a remote contractor environment
• ✓Availability for at least 6 hours per day and a minimum of 40 hours per week
• ✓Able to provide 4 hours of overlap with PST working hours

Relevant backgrounds mentioned

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