Granica is an AI research and systems company building the infrastructure for a new kind of intelligence: one that is structured, efficient, and deeply integrated with data.
Our systems operate at exabyte scale, processing petabytes of data each day for some of the world’s most prominent enterprises in finance, technology, and industry. These systems are already making a measurable difference in how global organizations use data to deploy AI safely and efficiently.
We believe that the next generation of enterprise AI will not come from larger models but from more efficient data systems. By advancing the frontier of how data is represented, stored, and transformed, we aim to make large-scale intelligence creation sustainable and adaptive.
Our long-term vision is Efficient Intelligence: AI that learns using fewer resources, generalizes from less data, and reasons through structure rather than scale. To reach that, we are first building the Foundational Data Systems that make structured AI possible.
The MissionAI 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.
This engineering team partners closely with the Granica 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.
What You’ll BuildGlobal Metadata Substrate. Architect the transactional and metadata substrate that supports time-travel, schema evolution, and atomic consistency across petabyte-scale tabular datasets.
Adaptive Engines. Build systems that reorganize data autonomously, learning from access patterns and workloads to maintain peak efficiency without manual tuning.
Intelligent Data Layouts. Optimize bit-level organization (encoding, compression, layout) to extract maximal signal per byte read.
Autonomous Compute Pipelines. Develop distributed compute systems that scale predictively, adapt to dynamic load, and maintain reliability under failure.
Research to Production. Implement new algorithms in compression, representation, and optimization emerging from ongoing research. Opportunities to publish and open-source are encouraged.
Latency as Intelligence. Design for minimal time between question and insight, enabling models and humans to learn faster from data.
Depth in distributed systems: consensus, partitioning, replication, fault tolerance.
Experience with columnar formats such as Parquet or ORC and low-level encoding strategies.
Understanding of metadata-driven architectures and adaptive query planning.
Production experience with Spark, Flink, or custom distributed engines on cloud object storage.
Proficiency in Java, Rust, Go, or C++ with an emphasis on clarity and quality.
Curiosity about theory of the mathematics of compression, entropy, and learning efficiency.
A builder’s mindset: pragmatic, rigorous, and grounded in long-term systems thinking.
Familiarity with Iceberg, Delta Lake, or Hudi.
Research or open-source contributions in compression, indexing, or distributed computation.
Interest in how data representation affects training dynamics and model reasoning efficiency.
Fundamental Research Meets Enterprise Impact. Work at the intersection of science and engineering, turning foundational research into deployed systems serving enterprise workloads at exabyte scale.
AI by Design. Build the infrastructure that defines how efficiently the world can create and apply intelligence.
Real Ownership. Design primitives that will underpin the next decade of AI infrastructure.
High-Trust Environment. Deep technical work, minimal bureaucracy, shared mission.
Enduring Horizon. Backed by NEA, Bain Capital, and various luminaries from tech and business. We are building a generational company for decades, not quarters or a product cycle.
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
Join us to build the foundational data systems that power the future of enterprise AI.
At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring.
Top Skills
What We Do
Massive-scale data should be an asset — not a liability.
At Granica, we’re building a state-of-the-art AI efficiency, data optimization and compression platform designed to make cloud-scale data cheaper, faster, safer and more intelligent.
As enterprises generate and store unprecedented volumes of data, traditional infrastructure can’t keep up — costs explode, performance lags, and privacy becomes harder to enforce. Granica changes that. We sit beneath the lakehouse — optimizing the data itself through advanced lossless compression, intelligent data selection, and built-in privacy preservation. Our platform enables enterprises to cut cloud data costs by up to 80% while improving performance and reducing operational complexity.
This is deep infrastructure — already deployed in live production environments, operating across hundreds of petabytes of enterprise data.
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, cloud infrastructure, and security
* A shared obsession with performance, scale, and clean design
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Granica Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
