Scientific Computing Specialist (Remote)

Job summary

Create self-contained terminal benchmark tasks based on authentic Earth-science workflows, including preparing datasets and building reproducible computational environments. Develop expert solutions, objective grading criteria, automated tests, and documentation, while validating scientific assumptions, data quality, and numerical accuracy.

Job details

Role Title: Scientific Computing Specialist (Remote) Role Type: Contractor assignment (no medical/paid leave) Location: Remote Role Overview: We are seeking Earth Sciences experts to develop realistic, terminal-based scientific tasks for Terminal Bench Science. You will create computational workflows involving environmental data, climate systems, atmospheric processes, geophysics, oceanography, geology, and related fields. You will design tasks that require AI agents to inspect scientific datasets, process geospatial or time-series data, run models, troubleshoot pipelines, and generate objectively verifiable scientific outputs. What you'll do: 1. Translate authentic Earth-science workflows into self-contained terminal benchmark tasks. 2. Prepare geospatial, climate, atmospheric, geological, hydrological, or oceanographic datasets. 3. Build reproducible computational environments with appropriate scientific libraries and command-line tools. 4. Create expert solutions using Python, R, Bash, Julia, or domain-specific software. 5. Develop tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, or environmental risk analysis. 6. Define objective grading criteria for scientific outputs, model behavior, data transformations, and spatial or temporal accuracy. 7. Validate coordinate systems, units, timestamps, missing-data handling, and scientific assumptions. 8. Create automated tests for numerical tolerances, file formats, metadata, and reproducibility. 9. Debug issues involving geospatial projections, large datasets, dependencies, performance, and numerical stability. 10. Document input data provenance, expected outputs, edge cases, and limitations. What we're looking for: 1. Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences or a closely related field. 2. 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. 3. Strong programming skills in Python, R, Julia, Bash, or another scientific programming language. 4. Hands-on experience with scientific data processing, numerical modeling, geospatial analysis, environmental datasets, or time-series analysis. 5. Comfortable working independently in Linux/terminal-based environments. 6. Ability to build, debug, and validate reproducible scientific computational workflows. 7. Strong understanding of scientific quality control, spatial/temporal data, uncertainty, and numerical accuracy. Preferred Qualification: 1. Experience with NumPy, pandas, SciPy, xarray, rasterio, GeoPandas, Cartopy, GDAL, or similar scientific/geospatial tools. 2. Experience with scientific data formats such as NetCDF, HDF5, GeoTIFF, shapefiles, or GRIB. 3. Experience working with climate, weather, satellite, seismic, oceanographic, geological, or hydrological datasets. 4. Familiarity with Docker, Conda, Git, CI/CD, automated testing, or HPC environments. Nice to Have: 1. Research software engineering, scientific benchmarking, or automated grader development experience. 2. Experience evaluating AI coding/terminal agents or developing tasks and evaluations for AI systems. 3. Publications or open-source contributions in Earth, environmental, geospatial, or computational sciences. Offer Details: 1. Commitments Required: At least 6 hours per day and minimum 40 hours per week with overlap of 4 hours with PST. 2. Employment type : Contractor assignment (no medical/paid leave) 3. Duration of contract : 5 week [expected start date is next week] 4. Payment type: Pay Per Task $300/per aproved task What You’ll Do 1. Translate authentic Earth-science workflows into self-contained terminal benchmark tasks. 2. Prepare geospatial, climate, atmospheric, geological, hydrological, or oceanographic datasets. 3. Build reproducible computational environments with appropriate scientific libraries and command-line tools. 4. Create expert solutions using Python, R, Bash, Julia, or domain-specific software. 5. Develop tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, or environmental risk analysis. 6. Define objective grading criteria for scientific outputs, model behavior, data transformations, and spatial or temporal accuracy. Apply Now!

What you’ll do

Create self-contained terminal benchmark tasks based on authentic Earth-science workflows, including preparing datasets and building reproducible computational environments. Develop expert solutions, objective grading criteria, automated tests, and documentation, while validating scientific assumptions, data quality, and numerical accuracy.

Requirements

Candidates need a Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences or a related field, with strong expertise in at least one Earth-science discipline. They must have scientific programming and data-processing experience, be comfortable working independently in Linux terminal environments, and be able to build, debug, and validate reproducible workflows.

Listed skills

• Python · Preferred
• Linux · Preferred
• Automated testing · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Earth Sciences
• Scientific Computing
• Python
• R
• Julia
• Bash
• Geospatial Analysis
• Climate Science
• Numerical Modeling
• Time-Series Analysis
• Remote Sensing
• Scientific Data Processing
• Linux
• Reproducible Workflows
• Automated Testing
• AI Benchmarking

Job areas

• Science & Research
• Environmental & Sustainability
• Technology
• Data & Analytics
• Software

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