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.
RoleYou 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 ResponsibilitiesRepresentation 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 BackgroundExperience• 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

Department: Dhaarini
Experience: 3-5
Posted: 2026-06-19T05:56:34.513Z