Job Details

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.

Job Summary

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, we'd 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 We're 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 Shapefiles)
- 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Geospatial Machine Learning Engineer (Bengaluru)
🏢 Alt Carbon
📍 Bengaluru