Finance Manager
satsure
ML Researcher – Foundation Models
satsure· Dhaarini
About this role
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
Frequently Asked Questions
Is the salary disclosed for the ML Researcher – Foundation Models position at satsure?
The salary for this ML Researcher – Foundation Models role at satsure is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Where is the ML Researcher – Foundation Models position at satsure located?
This ML Researcher – Foundation Models role at satsure is based in Bangalore, India. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
Is the ML Researcher – Foundation Models role at satsure full-time or part-time?
This is listed as a FULL TIME position. It is posted as a ML Researcher – Foundation Models role in the Dhaarini department at satsure.
Which team or department does the ML Researcher – Foundation Models at satsure belong to?
This ML Researcher – Foundation Models position is part of the Dhaarini department at satsure. See the full job description for more information about the team structure and responsibilities.
How do I apply for the ML Researcher – Foundation Models position at satsure?
Click the "Apply Now" button on this page. You will be redirected to satsure's official application portal hosted on keka where you can submit your application directly.
When was the ML Researcher – Foundation Models job at satsure posted?
This ML Researcher – Foundation Models position at satsure was posted on Jun 19, 2026. Apply as soon as possible — early applications are often reviewed first.
ML Researcher – Foundation Models
satsure
You'll be redirected to satsure's official application page on keka.