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