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 Preferred Background Domain Expertise • 3–5 years of experience in Applied Data Science at scale • Strong understanding of remote sensing fundamentals, including: • Atmospheric correction • SAR backscatter • Orthorectification • Familiarity with multi-sensor data (optical, SAR, DEM, etc.) Data Engineering at Scale • Experience working with large-scale (TB–PB) datasets across the ML lifecycle • Hands-on experience with: • Distributed data processing • Efficient storage and retrieval strategies • Understanding of how data pipelines interact with model training workflows Tooling (Geo Stack) • Experience with geospatial data tooling, such as: • Xarray, Dask, Rasterio, Zarr • Google Earth Engine (nice to have) Mindset • Strong data intuition—ability to reason about bias, coverage, and representativeness • Systems thinking: understands how data decisions impact model behavior at scale • Comfortable working in ambiguous, evolving problem spaces Benefits: • Medical Health Cover for you and your family, including unlimited online doctor consultations • Access to mental health experts for you and your family • Dedicated allowances for learning and skill development • Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, and bereavement leaves Interview Process: • Intro call • Assessment • Presentation • Interview rounds (ideally up to 3-4 rounds) • Culture Round / HR round