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Granica
San Francisco Bay Area, United States
Source: Granica careers · View original posting
From Granica's posting. “We” and “our” refer to the employer.
Mountain View, CA (On-site)
Overview
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:
How can we build generative AI that learns efficiently from tabular data?
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 data.
Design rigorous experiments and benchmarks to measure progress.
Collaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems.
Compensation
, and scale both efficiently.
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