ML Researcher – 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 You will be the architect of the model’s latent space , designing foundation models for multi-spectral, multi-temporal, and multi-resolution geospatial data . This is a hands-on role involving prototyping, experimentation, and large-scale training. You will work across representation learning, model scaling, and spatiotemporal modeling to build systems that generalize across sensors, geographies, and time. Key Responsibilities Representation Learning Design and implement self-supervised learning (SSL) objectives (e.g., Masked Autoencoders, DINO-style methods, contrastive learning) tailored for geospatial data Develop multi-modal representations spanning optical, SAR, elevation, and derived signals Ensure representations transfer effectively across tasks such as segmentation, classification, and change detection Design evaluation strategies to measure generalization across geographies, sensors, and time Model Development & Scaling Design and scale models based on Vision Transformers (ViT), hybrid architectures, or State Space Models (e.g., Mamba) to large parameter regimes Apply modern training techniques such as RMSNorm, FlashAttention, mixed precision, and gradient checkpointing Run scaling experiments, ablations, and architecture explorations grounded in empirical rigor Leverage insights from scaling behavior to make compute-efficient decisions across model size, data, and training strategy Temporal Dynamics Develop methods to model time-series satellite data , capturing: Seasonal patterns Temporal dependencies Long-term land-use changes Explore sequence modeling, memory mechanisms, and temporal tokenization strategies Systems-Level Thinking Design ML systems as end-to-end pipelines (data ingestion → curation → training → evaluation → deployment → feedback) Make explicit trade-offs between model quality, latency, cost, and data freshness Work with platform teams to optimize: Distributed training (FSDP, DeepSpeed) GPU utilization Data pipelines and experiment throughput Build reusable components and abstractions , not one-off models Preferred Background Experience 3–5 years of experience in ML research or applied research roles Experience in large-scale foundation model development (vision, multimodal, speech, or related domains)…