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)
• •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