Scientific Computing / Research Engineering Expert — Earth Sciences (Terminal Bench Science)

About Turing: Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies. Role Overview: We are seeking experienced Earth Sciences experts to develop realistic, terminal-based scientific and computational tasks for a frontier AI training and evaluation initiative. In this role, you will design challenging scientific tasks involving climate science, atmospheric processes, geophysics, oceanography, geology, hydrology, remote sensing, and environmental modeling. You will build reproducible computational workflows that require AI agents to analyze scientific datasets, execute code, run simulations, troubleshoot technical issues, and generate objectively verifiable scientific outputs. What you'll do: Design realistic, multi-step scientific tasks based on authentic Earth Sciences, climate, geospatial, environmental, and geophysical research workflows . Prepare and process scientific datasets involving climate, weather, atmospheric conditions, oceanography, geology, hydrology, satellite imagery, and environmental systems. Develop computational tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, and environmental risk analysis . Build reproducible, terminal-based computational environments using scientific libraries, command-line tools, and appropriate dependencies. Develop expert reference solutions using Python, R, Bash, Julia, or domain-specific scientific software . Create automated tests and evaluation criteria to verify scientific correctness, numerical accuracy, spatial and temporal consistency, and reproducibility. Validate coordinate reference systems, measurement units, timestamps, missing-data handling, uncertainty, and scientific assumptions. Troubleshoot technical issues involving geospatial projections, large scientific datasets, software dependencies, computational performance, and numerical stability. Ensure tasks require meaningful multi-step scientific reasoning and cannot be solved through simple shortcuts or hard-coded outputs. Document dataset sources, computational workflows, expected outputs, edge cases, and scientific limitations. What we're looking for: Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences, Climate Science, Atmospheric Science, Geophysics, Oceanography, Geology, Hydrology, Environmental Science, or a closely related discipline. Strong programming skills in Python, R, Bash, Julia, or another relevant scientific programming language . Hands-on experience working in Linux and terminal-based computational environments . Practical expertise in at least one major area: scientific data processing, numerical modeling, geospatial analysis, climate modeling, remote sensing, or environmental data analysis . Strong understanding of spatial datasets, time-series analysis, scientific modeling, numerical accuracy, uncertainty, and data quality control. Experience working with real-world scientific datasets, including geospatial, climate, atmospheric, oceanographic, geological, or hydrological data. Ability to independently develop, execute, debug, and validate reproducible scientific computational workflows. Strong analytical and problem-solving skills with the ability to identify scientific inaccuracies, data inconsistencies, and computational errors. Ability to design objective validation methods and clearly document technical solutions and scientific assumptions. Nice to have: Experience with scientific computing and geospatial libraries such as NumPy, pandas, SciPy, xarray, rasterio, GeoPandas, Cartopy, GDAL, or similar tools . Familiarity with scientific data formats such as NetCDF, HDF5, GeoTIFF, shapefiles, GRIB , and other geospatial or environmental data formats. Experience working with climate, weather, oceanographic, seismic, satellite, hydrological, or geological datasets . Familiarity with Docker, Conda, Git, CI/CD pipelines, and automated testing frameworks . Experience using high-performance computing (HPC) environments or processing large-scale scientific datasets. Experience with Earth-system models, environmental forecasting, remote sensing applications, or geophysical simulations. Research software engineering experience, including developing scientific tools, simulation pipelines, or reproducible research workflows. Experience developing or evaluating AI coding agents, terminal-based agents, scientific benchmarks, or automated grading systems . Publications, research contributions, or open-source development experience in Earth or Environmental Sciences. Prior experience in AI training, scientific quality control, benchmark development, or evaluation programs. Offer Details: Commitments Required: At least 4 hours per day and minimum 40 hours per week with overlap of 4 hours with PST. Employment type : Contractor assignment (no medical/paid leave) Duration of contract : 4 week [expected start date is next week]