Geospatial Machine Learning Engineer

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.

Role Overview

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!

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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