Research Scientist – Diffusion Models

About Granica

Granica is building the efficiency and intelligence layer for enterprise AI.

Crunch makes massive enterprise data cheaper and easier to operate.

Large Tabular Models learn from structured data to support shared intelligence across many capabilities.

Myelin makes long-running AI agents more efficient and durable.

Granica has processed hundreds of petabytes of tabular data in production

, and our research is led by

Stanford Professor Andrea Montanari.

Location:

Mountain View, CA (On-site)

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.