Research Scientist - Mountain View, CA

The Mission

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.

• PhD in Machine Learning, Statistics, Applied Mathematics, or a related field with specialization in structured, tabular, or relational data modeling
  .

• Research or applied work in areas such as representation learning, generalization theory, probabilistic modeling, or foundational models
  .

• Strong grounding in information theory, optimization, or statistical inference
  .

• Hands‑on experience with deep learning frameworks such as PyTorch, JAX, or Tensor Flow, and proficiency in Python or Rust for large‑scale experimentation.

• Demonstrated ability to translate theoretical ideas into performant, reliable systems.

• Curiosity about how structure and relational information can drive new forms of generalization and reasoning in AI.

• A pragmatic, impact‑driven approach to research: you care about elegance, but you ship results that work at scale.

• Research experience in structured representation learning, embeddings, or model architectures for tabular and multimodal data.

• Familiarity with distributed data systems, query engines, or large‑scale learning infrastructure.

• Contributions to open‑source projects or collaborative research bridging theory and production.

• Competitive salary, meaningful equity, and substantial bonus for top performers

• Flexible time off plus comprehensive health coverage for you and your family

• Support for research, publication, and deep technical exploration

At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring.
Join us to build the foundational data systems that power the future of enterprise AI!