Earth Sciences AI Task Development Expert

OpenTrain AI, Inc.; United States; Remote; USD 55,000 - 96,000; Part time

Job summary

OpenTrain AI, Inc. is seeking a remote contractor to translate real Earth sciences workflows into terminal-based tasks for training and evaluating AI systems, requiring meaningful reasoning, coding, testing, and verifiable results.

You will design multi-step climate and geoscience tasks, process diverse environmental datasets, build reproducible terminal environments, and produce reference solutions in Python, R, Bash, Julia, or related tools.

Qualifications

• Ph.D., postdoctoral experience, or equivalent advanced technical experience in Earth Sciences or closely related fields.
• Strong programming ability in Python, R, Bash, Julia, or another scientific language.
• Experience with Linux and terminal-based computational environments.
• Advanced knowledge of Earth sciences and data processing, numerical modeling, geospatial analysis.
• Familiarity with scientific libraries including NumPy, pandas, SciPy, xarray, rasterio, GeoPandas, Cartopy, GDAL, NetCDF, HDF5, GeoTIFF.

Responsibilities

• Design multi-step tasks covering climate science, atmospheric processes, geophysics, oceanography, geology, hydrology, remote sensing, and environmental modeling.
• Prepare and process climate, weather, atmospheric, oceanographic, geological, hydrological, satellite, and other environmental datasets.
• Develop geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, or environmental risk tasks.
• Build reproducible terminal environments with scientific libraries, command-line tools, and required dependencies.
• Create reference solutions with Python, R, Bash, Julia, or relevant scientific software.
• Write automated tests and evaluation criteria for scientific correctness, numerical accuracy, spatial and temporal consistency, and reproducibility.
• Check coordinate reference systems, units, timestamps, missing data, uncertainty, and scientific assumptions.
• Document data sources, workflows, expected outputs, edge cases, and scientific limitations.

Skills

Earth science knowledge

Python

R

Bash

Julia

Linux

Scientific programming

Data processing

Education

PhD or equivalent

Tools

Docker

Conda

Git

CI/CD

xarray

GeoPandas

GDAL

NetCDF

Job description

The work

You will turn real Earth sciences and computational research workflows into terminal-based tasks for training and evaluating AI systems. Each task should require meaningful scientific reasoning, code execution, troubleshooting, and objectively verifiable results.

• Design multi-step tasks covering climate science, atmospheric processes, geophysics, oceanography, geology, hydrology, remote sensing, and environmental modeling.
• Prepare and process climate, weather, atmospheric, oceanographic, geological, hydrological, satellite, and other environmental datasets.
• Develop geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, and environmental risk tasks.
• Build reproducible terminal environments with scientific libraries, command-line tools, and required dependencies.
• Create reference solutions with Python, R, Bash, Julia, or relevant scientific software.
• Write automated tests and evaluation criteria for scientific correctness, numerical accuracy, spatial and temporal consistency, and reproducibility.
• Check coordinate reference systems, units, timestamps, missing data, uncertainty, and scientific assumptions.
• Document data sources, workflows, expected outputs, edge cases, and scientific limitations.

What it pays and takes

This is a remote contractor assignment open worldwide. The work requires advanced Earth sciences knowledge, scientific programming, and experience with Linux and terminal-based computational environments.

• Pay information is not provided in the listing.
• 20+ hours per week.
• Minimum four-hour daily commitment and four hours of overlap with Pacific Time.
• Fluent English.
• 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 field.
• Strong programming ability in Python, R, Bash, Julia, or another relevant scientific language.
• Practical expertise in scientific data processing, numerical modeling, geospatial analysis, climate modeling, remote sensing, or environmental data analysis.
• Strong understanding of spatial datasets, time-series analysis, numerical accuracy, uncertainty, data quality control, and reproducible workflows.
• Ability to independently develop, debug, validate, and clearly document computational solutions.
• Helpful tools and formats include NumPy, pandas, SciPy, xarray, rasterio, GeoPandas, Cartopy, GDAL, NetCDF, HDF5, GeoTIFF, shapefiles, GRIB, Docker, Conda, Git, CI/CD, and automated testing frameworks.
• Additional useful experience includes high-performance computing, Earth-system models, environmental forecasting, geophysical simulations, research software engineering, scientific benchmarks, automated grading, AI coding agents, or scientific quality control.

About AI training work

AI training is the human work behind systems that learn from examples. In this role, your scientific tasks, reference solutions, and evaluations help measure whether AI can produce reliable computational results in Earth sciences.

People with advanced technical knowledge are needed because they can judge scientific assumptions, numerical accuracy, reproducibility, and the limits of model-generated work.