Granica reduces the cost of enterprise AI across data storage, data processing, and agent compute. Everything runs inside your perimeter, and you keep the intelligence your data builds. Backed by NEA.
About the role
Most of today's generative AI is built for text, images, and video. The world's most valuable data lives in tables: customer records, transactions, financial systems, telemetry, operational data, and business workflows. Today's generative AI stack wasn't designed to learn efficiently from this kind of information.
What they're looking for
- PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field
- Strong research record in machine learning
- Experience developing new models or learning algorithms
- Hands-on experience with PyTorch or JAX
- Strong programming skills in Python
- Ability to turn research ideas into working systems
More about this role
Most of today's generative AI is built for text, images, and video.
Enterprise data isn't.
The world's most valuable data lives in tables: customer records, transactions, financial systems, telemetry, operational data, and business workflows. Today's generative AI stack wasn't designed to learn efficiently from this kind of information.
At Granica, we're building Large Tabular Models (LTMs) —foundation models that learn natively from structured and relational enterprise data.
Our research is led by Prof. Andrea Montanari (Stanford) and focuses on one central question:
That requires solving problems well beyond model architecture, including intelligent data selection, dataset augmentation, representation learning, and information-preserving compression.
If you're excited about inventing the algorithms that make Large Tabular Models possible, we'd love to talk.
Develop new machine learning algorithms for Large Tabular Models.
Research methods for selecting, augmenting, and compressing training data without losing information.
Build representation learning techniques for structured and relational datasets.
Prototype and evaluate new approaches for generative modeling over enterprise...
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