Opportunity brief
About SatSure SatSure is a deep-tech decision intelligence company operating at the nexus of agriculture, infrastructure, and climate action. We turn earth observation data into actionable insights for governments, financial institutions, and enterprises across the developing world — at scale, with reliability. 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. 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 5–8 years of experience in ML research or applied research roles Experience in large-scale foundation model development (vision, multimodal, speech, or related domains) Experience training and/or fine-tuning billion-parameter models Experience working with sequence, video, or temporal data Exposure to geospatial foundation models such as: Prithvi Clay Segment Anything Model (SAM) (nice to have) Technical Skills Expert-level proficiency in PyTorch or JAX Strong experience with: Distributed training (FSDP / DeepSpeed) Large-scale datasets and training pipelines Familiarity with transformer architectures and training dynamics Bonus: CUDA / performance optimization experience Additional Strengths Familiarity with efficient scaling techniques (e.g., Mixture of Experts ) is a plus Strong experimental rigor and ability to design meaningful ablations Track record of publishing or contributing to state-of-the-art research in representation learning or generative modeling