Job description

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. 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.

Requirements

Must-haves

Diffusion models have transformed image, video, and multimodal AI.
We're applying those ideas to one of the next frontiers in machine learning.
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.
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.
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.

Nice-to-haves

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.

Application prep

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Keywords to mirror

aimachine learningpythonml

Platform: ashby
Effort: quick

Benefits

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Vision

401(K)

Pto