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 ResearchInvent 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
Contribute to the development of large tabular models and structured foundation models
Design architectures integrating relational, symbolic, and neural learning components
Research and implement methods for dataset compression, selection, and representation to improve learning efficiency
Develop cost models and optimization frameworks for large-scale structured learning systems
Collaborate closely with the Granica research group led by Prof. Andrea Montanari (Stanford) and with systems engineers
Rapidly prototype new algorithms and evaluate them on real enterprise datasets
Publish and contribute to the broader research community shaping the future of structured AI and efficient ML systems
PhD in Machine Learning, Statistics, Computer Science, Applied Mathematics, or a related field
Research experience related to structured, relational, or tabular data
Experience in one or more of the following areas:
Tabular or relational machine learning
Representation learning for structured data
Statistical learning theory or generalization
Probabilistic modeling or Bayesian inference
Optimization for machine learning
Scalable or distributed ML systems
Experience working with structured datasets or relational data systems
Strong grounding in statistics, optimization, information theory, or probabilistic inference
Hands-on experience with PyTorch, JAX, or TensorFlow
Strong programming skills in Python or Rust
Demonstrated ability to translate theoretical ideas into working systems or prototypes
Curiosity about how structure and relational information enable new forms of learning and reasoning
A pragmatic research mindset: you value elegant ideas but also ship systems that work at scale
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
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!
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.
