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# Research Scientist - Large Tabular Models

- **Company:** GRANICA LLC
- **Location:** United States
- **Contract:** Permanent contract
- **Skills:** Training Data, Computer Programming, Python (Programming Language), Machine Learning, Open Source Technology, Pytorch, Information Technology, Machine Learning Operations, Stable Diffusion
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/research-scientist-large-tabular-models-ltms-granica-ai-8129642

## About the Role

* 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.
 * Experience in structured learning, representation learning, generative modeling, probabilistic modeling, statistical learning, or scalable ML systems is particularly relevant.

Bonus

 * Research on tabular, relational, or graph data.
 * Experience with diffusion or other generative modeling approaches.
 * Publications at NeurIPS, ICML, ICLR, COLT, KDD, or related venues.
 * Open-source or production ML systems experience.

## Description

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.

What You'll Work On

 * 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.


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