Opportunity brief
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