Research Scientist – Diffusion Models

Granica; San Francisco (CA); On-site; USD 170,000 - 250,000; Full time

Benefits offered by this job

Competitive salary

Equity

Performance bonus

401(k) with company match

Health coverage

Daily catered meals

Unlimited PTO

Job summary

Granica, based in Mountain View, CA, is seeking a PhD-level ML researcher to advance diffusion models and Large Tabular Models (LTMs). You will work closely with Prof. Andrea Montanari and Granica’s research team to translate cutting-edge research into scalable production systems.

The role focuses on developing novel diffusion models, exploring representation learning, and designing efficient training for large-scale generative models, with strong emphasis on experimental rigor and benchmarks.

Qualifications

• PhD in Machine Learning, CS, Statistics, or related field.
• Strong research track record in generative ML.
• Experience building new generative models or learning algorithms.
• Hands-on experience with PyTorch or JAX.
• Strong programming skills in Python.
• Ability to translate research ideas into working systems.

Responsibilities

• 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 with Prof. Andrea Montanari and Granica’s research team to translate research into production systems.

Skills

PyTorch

JAX

Python

Generative ML

Research

Education

PhD in Machine Learning

Tools

PyTorch

JAX

Job description

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 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.

• 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.