Research Scientist role developing novel diffusion and generative learning models for Large Tabular Models, which learn from enterprise data. Work includes representation learning, efficient training for large-scale generative models, and benchmarks, within Prof. Andrea Montanari's research team. Stack: Python with PyTorch or JAX.

Research Scientist – Diffusion Models

Location: Mountain View, CA — Downtown HQ

Employment Type: Full-time

Work Model: On-site, 5 days/week

Department: Research

About Granica

Granica helps enterprises run AI for less, from the data layer to agent execution.

Granica is building across three related layers:

Crunch reduces storage and processing costs across enterprise data infrastructure.
Large Tabular Models unlock predictions and generated data from governed enterprise tables.
Myelin reduces token and compute overhead for long-running AI agents.

Everything runs inside the customer’s perimeter, so enterprises retain control of their data and the intelligence it creates.

Granica has already processed hundreds of petabytes of enterprise data in production for customers including Snap, Pinterest, Lyft, and ShareChat.

Our research organization is led by Stanford Professor Andrea Montanari.

We operate at the intersection of data infrastructure, AI systems, and applied research, building the efficiency and intelligence layer for enterprise AI.

Role Overview

Diffusion models have transformed image, video, and multimodal AI.

We're applying those ideas to one of the next frontiers in machine learning.

At Granica, we're building Large Tabular Models (LTMs)—foundation models designed to learn natively from enterprise data. Realizing that vision requires new generative modeling techniques capable of learning from structured information at scale.

Our research team is led by Prof. Andrea Montanari (Stanford) and explores a fundamental question:

How can diffusion models enable the next generation of AI for enterprise data?

If you're excited about inventing new generative learning algorithms and applying them to entirely new domains, we'd love to talk.

What You'll Work On

Develop novel diffusion models and generative learning algorithms.
Research new representation learning techniques for Large Tabular Models.
Design efficient training methods for large-scale generative models.
Prototype and evaluate new generative modeling approaches.
Design rigorous experiments and benchmarks to measure model quality and efficiency.
Collaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems.

What We're Looking For

PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field preferred; equivalent research and/or industry experience and demonstrated contributions to advancing state-of-the-art AI models will also be considered.
Strong research record in generative machine learning.
Experience developing new generative 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 with diffusion models, score-based generative modeling, representation learning, probabilistic modeling, or scalable ML systems is particularly relevant.

Bonus

Research applying diffusion models beyond traditional vision tasks.
Publications at NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related venues.
Open-source or production ML systems experience.

Compensation & Benefits

Competitive salary, meaningful equity, and performance bonus for top performers
401(k) with company match, comprehensive health coverage, and unlimited PTO
Daily catered meals in our Mountain View office
Support for research, publication, and conference participation

At Granica, you'll help build the next generation of enterprise AI—from exabyte-scale data infrastructure, Large Tabular Models (LTMs), and stateful AI agents. Together, we're creating the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.

Compensation: $160K - $240K