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About Gramian
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
About The Role
We are looking for an Earth Sciences expert to develop realistic, terminal-based scientific tasks for an AI benchmarking project. You will translate authentic workflows across climate science, atmospheric science, geophysics, oceanography, geology, hydrology, remote sensing, and environmental modeling into reproducible computational tasks that AI agents can execute and solve.
The role combines scientific expertise, programming, data analysis, and computational workflow design, with a strong focus on creating objectively verifiable outputs and robust evaluation criteria.
Key Responsibilities
Translate authentic Earth-science workflows into self-contained terminal-based benchmark tasks
Prepare and structure geospatial, climate, atmospheric, geological, hydrological, or oceanographic datasets
Build reproducible computational environments using scientific libraries and command-line tools
Develop expert reference solutions using Python, R, Bash, Julia, or domain-specific software
Design tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, and environmental risk analysis
Define objective grading criteria for scientific outputs, 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 projections, large datasets, dependencies, performance, and numerical stability
Document data provenance, expected outputs, edge cases, assumptions, and limitations
Requirements
Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences or a closely related field
Deep 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
Experience working in Linux or 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
Work arrangement
Yes
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