About the Role
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
5–8 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, bereavement leaves
Interview Process
Intro call
Assessment
Presentation
Interview rounds (ideally up to 3-4 rounds)
Culture Round / HR round
Show more
Show less