Research Scientist – Diffusion Models

Granica
Bay Area Office, US
On-site

Who this role is best for

Aimed at senior or mid-level researchers in generative machine learning who can work on-site in the Bay Area and contribute to enterprise AI infrastructure.

Best fit for

  • Senior researchers with a strong background in generative models and a desire to work on enterprise AI infrastructure
    — “At Granica, you'll help build the next generation of enterprise AI”
  • Candidates with experience in developing novel generative learning algorithms and translating research into production systems
    — “Ability to turn research ideas into working systems”
  • Individuals with a proven research record in diffusion models and probabilistic modeling techniques
    — “Experience with diffusion models, score-based generative modeling, representation learning, probabilistic modeling, or scalable ML systems is particularly relevant”

Things to consider

  • On-site presence in Mountain View, CA is required despite the lack of explicit remote flexibility mention
    — “Location: Mountain View, CA (On-site)”
  • The role demands a high level of collaboration with academic researchers and engineering teams
    — “Collaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems”

How to stand out

  • Emphasize experience with PyTorch or JAX in your resume and interview responses
    — “Hands-on experience with PyTorch or JAX”
  • Highlight any research contributions to generative models or diffusion-based techniques
    — “Strong research record in generative machine learning”
  • Demonstrate a history of designing efficient training methods for large-scale models
    — “Design efficient training methods for large-scale generative models”
  • Showcase your ability to create and evaluate benchmarks for model performance
    — “Design rigorous experiments and benchmarks to measure model quality and efficiency”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • 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
Typical background
PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related fieldStrong research record in generative machine learning

Skills & requirements

Required

Phd In Machine Learning, Computer Science, Statistics, Applied Mathematics, Or A Related FieldStrong Research Record In Generative Machine LearningExperience Developing New Generative Models Or Learning AlgorithmsHands-on Experience With Pytorch Or JAXStrong Programming Skills In PythonAbility To Turn Research Ideas Into Working Systems

Preferred

Research Applying Diffusion Models Beyond Traditional Vision TasksPublications At Neurips, Icml, Iclr, Cvpr, Iccv, Eccv, Or Related VenuesOpen-source Or Production ML Systems Experience

Stack & domain

PythonPyTorchJAXMachine LearningAIData Science

About the role

Original posting from Granica via Ashby

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.

Source: Granica careers (Ashby)

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