Senior ML Researcher – Foundation Models

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