About the job
About Granica
Granica is an innovative AI research and infrastructure firm dedicated to creating reliable and steerable representations of enterprise data.
We build trust through our product Crunch, a policy-driven health layer that ensures large tabular datasets remain efficient, reliable, and reversible. On this solid foundation, we are developing Large Tabular Models—systems designed to learn cross-column and relational structures in order to provide trustworthy answers and automation with inherent provenance and governance.
Our Mission
AI is currently hampered not only by the design of models but also by the inefficiencies of the data that supports them. Every redundant byte, poorly organized dataset, and inefficient data pathway contributes to significant costs, latency, and energy waste as we scale.
Granica aims to eliminate these inefficiencies. We merge cutting-edge research in information theory, probabilistic modeling, and distributed systems to craft self-optimizing data infrastructures: systems that consistently enhance the representation and utilization of information by AI.
Our engineering team collaborates closely with the Granica Research group led by Prof. Andrea Montanari of Stanford University, bridging advancements in information theory and learning efficiency with large-scale distributed systems. Together, we firmly believe that the next major advancement in AI will stem from breakthroughs in efficient systems rather than merely larger models.
Your Contributions
Global Metadata Substrate: Design a transactional and metadata substrate that facilitates time-travel, schema evolution, and atomic consistency across massive petabyte-scale tabular datasets.
Adaptive Engines: Develop systems that autonomously reorganize data, learning from access patterns and workloads to maintain peak efficiency without the need for manual tuning.
Intelligent Data Layouts: Optimize bit-level organization (including encoding, compression, and layout) to maximize signal extraction per byte read.
Autonomous Compute Pipelines: Create distributed compute systems that scale predictably, adapt to dynamic loads, and ensure reliability under failure conditions.
Research to Production: Apply new algorithms in compression, representation, and optimization that emerge from ongoing research. We encourage opportunities to publish and open-source your work.
Latency as Intelligence: Design systems that inherently minimize latency as a measure of intelligence.

