AI Soil Carbon Stock Estimation Engineer

Paleon Data

Remote · $60k – $80k / year · Full-time · Posted 4 months ago

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About the role

Soil stores more carbon than the atmosphere and all living vegetation combined. Knowing how much carbon is stored in a given parcel of agricultural or forested land — the soil organic carbon stock — is critical for carbon credit verification, regenerative agriculture program measurement, and national greenhouse gas inventories. Direct measurement requires soil core sampling, laboratory analysis, and expert interpretation: expensive, time-consuming, and spatially sparse. ML-based soil carbon estimation — combining satellite-derived spectral data, terrain analysis, climate records, and land use history to predict soil organic carbon without dense ground sampling — is an active research area. We build climate data products for research institutions and voluntary carbon market participants. Soil carbon estimation is a new product line we're developing. As a junior engineer, you'll build the data pipelines that feed the estimation models: satellite spectral data ingestion, terrain feature derivation from digital elevation models, climate variable aggregation, and the ground truth measurement database from published soil surveys. Senior data scientists design the models; you build the training data infrastructure that makes them possible.

Responsibilities

Build satellite spectral data ingestion pipeline for soil reflectance bands used in organic carbon proxies
Implement terrain feature derivation from digital elevation models: slope, aspect, curvature, drainage indices
Create climate variable aggregation integrating precipitation, temperature, and seasonal moisture patterns
Develop ground truth measurement database from published soil organic carbon surveys and field campaigns
Build model training data assembly joining spatial features with matched soil sampling measurements
Maintain pipeline as new satellite data sources and reference soil databases are added

Requirements

1+ year of Python and SQL development experience
Basic familiarity with geospatial data or remote sensing preferred
Interest in environmental data science, carbon markets, or climate informatics
Attention to spatial data accuracy and coordinate system consistency
Good communication for collaborating with senior data scientists on training data requirements