Machine Learning Engineer

Role & responsibilities

• Develop, train, and evaluate machine learning and deep learning models for geospatial and Earth Observation use cases
• Work with satellite imagery, raster and vector data, and spatio-temporal datasets
• Build and maintain end-to-end ML pipelines including preprocessing, training, inference, and validation
• Optimize models for performance, scalability, and production readiness
• Collaborate with GIS and backend engineers to integrate ML models into APIs and web platforms
• Translate research papers and proof-of-concept models into stable, maintainable production systems
• Participate in model benchmarking, documentation, and technical decision-making
• Contribute to ongoing R&D initiatives and experimentation

Preferred candidate profile

• Experience with

  geospatial or remote sensing data

  (GeoTIFF, NetCDF, STAC, raster/vector formats)
• Computer vision experience (segmentation, classification, object detection), especially on satellite imagery
• Exposure to Docker, REST APIs, or basic ML deployment workflows
• Familiarity with open-source GIS ecosystems
• Ability to read and implement research papers pragmatically
• Interest in climate, environment, forestry, agriculture, or urban analytics