Research Scientist - Applied AI

About Granica

Granica is an AI research and infrastructure company building reliable and steerable representations for enterprise structured data
.

The rarest thing in enterprise AI is durable access plus trust.
Crunch is how we earn it: a policy-driven physical health layer that keeps large tabular data estates efficient and reliable, safely and reversibly.

On top of that foundation, we’re building structured intelligence using Large Tabular Models
: systems that learn cross-column and relational structure to deliver trustworthy answers and automation with provenance and governance built in.

AI today is limited not only by model design but by the inefficiency of the data that feeds it. At scale, each redundant byte, each poorly organized dataset, and each inefficient data path slows progress and compounds into enormous cost, latency, and energy waste.

Granica’s mission is to remove that inefficiency. We combine new research in information theory
, probabilistic modeling
, and distributed systems to design self-optimizing data infrastructure: systems that continuously improve how information is represented and used by AI.

Granica’s Research group led by Prof. Andrea Montanari (Stanford), bridging advances in information theory and learning efficiency with large-scale distributed systems. Together, we share a conviction that the next leap in AI will come from breakthroughs in efficient systems, not just larger models.

Granica is pioneering a new class of structured AI models
: foundational models built to learn and reason from the world’s relational, tabular, and structured data. While others focus on unstructured text or media, we are exploring the next frontier: systems that understand and reason over the information that runs the global economy.

• Invent and prototype algorithms that define the foundations of structured AI
  , advancing representation learning and efficient information modeling for enterprise and tabular data at petabyte scale.

• Develop adaptive learners that fuse statistical learning theory with large-scale systems optimization, contributing to a new generation of foundational models for structured information
  .

• Design architectures that integrate symbolic, relational, and neural components, enabling AI systems to reason directly over structured enterprise data.

• Build cost models and optimization frameworks that make structured learning efficient, both computationally and economically.

• Collaborate closely with the Granica Research group led by Prof. Andrea Montanari (Stanford) and with systems engineers to transform theoretical ideas into production-grade systems used across live enterprise workloads.

• Iterate fast: prototype new model architectures, evaluate on live datasets, and publish results that advance both theory and practice.

• Contribute to the global research community shaping the future of structured AI and efficient learning.

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