Earth & Environmental Science Expert (AI Projects)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 16h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 8d ago
The date the source published, not the day we noticed it (2026-09-24). Last seen at its source just now.
We have tracked this listing since 25 Sep 2026 (7 days). The employer's own board has carried it every time we have read it, most recently just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Bangladesh, India, Indonesia, Egypt, Ghana, Nigeria
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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workable employer's own board first seen 7d ago · last seen just now
The listing
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.