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) • 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 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