Location: Mountain View, CA (On-site)
OverviewDiffusion 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 OnDevelop 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.
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.
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.
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.
Skills Required
- PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or 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
- 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
Granica Compensation & Benefits Highlights
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Healthcare Strength — Health coverage is described as premium medical, dental, and vision, with one listing noting fully paid employee premiums and 75% coverage for dependents. This depth of coverage is highlighted across company and job‑profile materials.
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Leave & Time Off Breadth — Time off is presented as unlimited PTO paired with paid holidays and sick time, plus quarterly company‑wide recharge days to encourage real downtime. The combination supports rest beyond flexibility alone.
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Strong & Reliable Incentives — Quarterly performance bonuses for all roles are consistently advertised as part of total rewards alongside competitive salary. This regular cadence signals dependable incentive pay rather than one‑off perks.
Granica Insights
What We Do
Our mission is to remove inefficiency from the foundation of AI. By combining new research in information theory, probabilistic modeling, and distributed systems, we’re creating self-optimizing data infrastructure that continuously improves how information is represented and used by intelligent systems.
Why Work With Us
We’re a tight-knit team combining --> * Fundamental research in compression, data systems, and information theory * World-class systems engineering across storage, infrastructure, and research led by our Chief Scientist & Stanford Prof. Andrea Montanari * A shared obsession with performance, scale, and clean design
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Granica Offices
OnSite Workspace
