Geospatial Machine Learning Enginee...

About Alt Carbon

Alt Carbon builds agri-infrastructure for climate action through technology like Enhanced Rock Weathering and Biochar. Our flagship initiatives, the Darjeeling Revival Project (DRP) and Bengal Renaissance Project (BRP), unite climate action with ecological restoration to turn underutilized land into carbon sinks across South Asia.

About the Team

Our 'Iris' team serves as Alt Carbon's engineering and AI backbone, building foundational data infrastructure to read Earth's soil, atmosphere, and geology at scale. We are laying the groundwork for Planetary Intelligence, turning fragmented observations into a unified understanding of our planet.

Role Overview

We seek a Geospatial ML Engineer to operationalize Planetary Intelligence by fusing multi-modal datasets (remote sensing, geochemistry, climate) into scalable pipelines. You will build robust, interpretable production models and transform research concepts into deployed systems that impact real-world environmental decisions.

Key Responsibilities

• Data Pipelining: Build scalable pipelines for multispectral, hyperspectral, atmospheric, and geology datasets from sources like NASA, ESA, USGS, ISRO, Copernicus, and FAO.
• Model Engineering: Harden prototype ML models for production using satellite imagery, soil chemistry, and weather patterns; maintain experiment tracking and model registries.
• Deployment & Scaling: Optimize and deploy models on cloud/on-prem infrastructure; implement monitoring for data drift with automated alerting.

Requirements

• B.Tech/M.Tech in ML, Data Science, CS, or Geoinformatics (2-3 years experience).
• Hands-on processing of geospatial data (Sentinel, Landsat, MODIS) and formats (GeoTIFF, NetCDF, Shapefiles).
• Proficiency in Python with PyTorch, scikit-learn, XGBoost/LightGBM.
• Familiarity with GDAL, rasterio, geopandas, xarray.
• AWS experience (Sagemaker, S3) and tooling like MLflow/Airflow.