SatSure - Applied Data Scientist - Geospatial Foundation Models

About SatSure SatSure is a deep tech, decision intelligence company working at the nexus of agriculture, infrastructure, and climate action - creating impact for the other millions, with a focus on the developing world. As part of this mission, we're building geospatial foundation models that learn directly from Earth observation data - optical, SAR, and elevation - at scale. This role sits at the heart of that effort: architecting and training large-scale models that can generalize across geographies, sensors, and time. You'll be shaping the core intelligence layer that powers insights for millions, not just fine-tuning someone else's model. Role In foundation model development, data is the moat. You will drive the transformation of petabytes of raw geospatial data into a high-quality, high-entropy training and evaluation corpus. This role sits at the intersection of remote sensing, data engineering, and ML, ensuring that models learn from diverse, representative, and well-curated data at scale. Key Responsibilities Data Curation & Pre-training Datasets - Design and implement data curation pipelines for large-scale pre-training datasets Develop sampling strategies to ensure: - Geographic and biome diversity - Coverage across seasons, sensors, and resolutions - Mitigate dataset biases (e.g., over-representation of cloud-free or high-income regions) - Balance trade-offs between data quality, diversity, and scale Evaluation Frameworks (Earth-Bench) - Design and own a comprehensive evaluation framework ("Earth-Bench") to assess: - Representation quality (post-SSL embeddings) Transfer performance on downstream tasks: - Segmentation - Yield prediction - Disaster mapping - Define metrics and benchmarks that reflect real-world generalization across geographies and time - Continuously evolve evaluation as new datasets, sensors, and tasks emerge Data Systems & Pipeline Thinking - Build and maintain scalable data pipelines for ingestion, processing, versioning, and access Work with ML and platform teams to: - Enable efficient data loading and training at scale - Optimize storage formats and access patterns (e.g., chunking, caching) Ensure datasets are: - Reproducible - Well-documented - Easily usable across teams Data-Centric ML Thinking - Analyze how data quality, diversity, and freshness impact model performance Partner with researchers to: - Identify failure modes driven by data gaps - Improve datasets to unlock model gains (not just model changes) - Treat data as a first-class lever for improving model quality