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, poorly organized dataset, and inefficient data path slows progress and compounds into enormous cost, latency, and energy waste.
Granica’s mission is to remove that inefficiency. We combine advances in
information theory, probabilistic modeling, and distributed systems
to design self-optimizing data infrastructure: systems that continuously improve how information is represented, compressed, and used by AI.
Granica’s research group is led by
Prof. Andrea Montanari (Stanford)
, bridging advances in learning theory and information efficiency with large-scale distributed systems. Together, we share a conviction that the next leap in AI will come not only from larger models, but from
more efficient learning systems and better data representations
.
Most modern AI research focuses on text, images, or video. Granica’s work focuses on the far less explored but economically critical domain of
large-scale structured and tabular data
, which powers the majority of enterprise decision-making systems.
Granica is pioneering a new class of
structured AI models
: foundational models built to learn and reason from 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 structured information that runs the global economy.
This role focuses specifically on machine learning for structured and tabular data rather than general LLM application development.
What You’ll Build and Research
- Invent and prototype algorithms that advance the foundations of machine learning for structured and tabular data
- Develop new representation learning techniques and information models for large enterprise datasets
- Build adaptive learners combining statistical learning theory, probabilistic modeling, and large-scale systems optimization
What You’ll Bring
Bonus
- Research in tabular machine learning, relational representation learning, or structured data modeling
- Experience building large-scale ML infrastructure or distributed training systems
- Familiarity with data systems, query engines, or dataset optimization pipelines
- Publications at top venues such as NeurIPS, ICML, ICLR, COLT, KDD, AAAI
- Contributions to open-source ML systems or research-to-production tooling
Compensation & Benefits
- 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!
Compensation Range: $160K - $250K
