Geospatial Machine Learning Engineer

Job Summary

Alt Carbon is building the infrastructure for Planetary Intelligence ; turning fragmented observations about soil, water, atmosphere, and rock into a unified, actionable understanding of Earth. We are looking for a Geospatial ML Engineer to make this real at the data and model layer: fusing multi-modal datasets across remote sensing, geochemistry, and climate systems into scalable pipelines, and building robust, interpretable ML models that work in production, not just in notebooks. If you get a thrill from turning a research paper into running code, wed like to talk!

Location: Bangalore, India

Experience: >2 years of experience in Machine Learning, Data Engineering, Geospatial Analytics, or related fields, with hands-on exposure to building and deploying ML systems using large-scale geospatial datasets.

About the Team

Every ML practitioners first dataset was Iris ; a small set of measurements that taught the world to classify what it could see. Our Iris team carries that spirit forward, only the dataset is the planet. We are Alt Carbons engineering and AI backbone, building the foundational data infrastructure and models to read the earths soil, atmosphere, and geology at scale. The work is early, the ambition is not ; were laying the groundwork for planetary intelligence, one pipeline at a time.

Responsibilities

• Data Pipelining
  • Build a scalable data pipeline for publicly available (e.g., NASA, ESA, USGS, ISRO, FAO, Copernicus) and proprietary multispectral, hyperspectral, atmospheric, and soil/geology datasets.
• Model Engineering
  • Work alongside data scientists and subject-matter experts to harden prototype ML models for production, leveraging multiple data modalities (satellite imagery, soil chemistry, weather patterns).
  • Build and maintain experiment tracking and model registries so every model version is traceable from training data to deployment.
• Deployment Scaling
  • Optimise and deploy models for large-scale inference on cloud/on-prem infrastructure.
  • Implement monitoring for data drift and model drift, with automated alerting.

What Were Looking For

• B.Tech/M.Tech in Machine Learning, Data Science, Computer Science, Geoinformatics etc. with >2 years experience as a Data Engineer/ML Engineer
• Hands-on experience with processing geospatial data (Sentinel, Landsat, MODIS, ERA5, etc.) and formats (GeoTIFF, NetCDF, ESRI Shape
• Proficiency in Python, with experience in ML/DL frameworks (PyTorch, scikit-learn, XGBoost, LightGBM).
• Familiarity with geospatial processing libraries (GDAL, rasterio, geopandas, xarray, rioxarray).
• Experience deploying models on cloud like AWS (Sagemaker, S3, EC2)
• Experience with ML lifecycle tooling (like MLflow) and workflow orchestration (like Airflow).

Nice to Haves

• Familiarity with cloud-native geospatial stacks (e.g., STAC catalogs, cloud-optimized formats).
• Contribution to open source ML projects.
• Understanding of soil science, climate modeling, or geology is a plus.

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