
Granica is a pioneer in AI-driven data optimization, delivering state-of-the-art technologies to help enterprises manage large-scale data volumes used in AI, machine learning, and…

Granica is a pioneer in AI-driven data optimization, delivering state-of-the-art technologies to help enterprises manage large-scale data volumes used in AI, machine learning, and…
What they do: AI Data Readiness Platform that optimizes and compresses large-scale data for AI/ML and analytics
Flagship product: Crunch — high-performance data compression and query performance solution
Founded / HQ: 2019; Mountain View, California
Founders: Rahul Ponnala (CEO) and Tarang Vaish (CTO)
Funding: $45M Series A (announced June 8, 2023)
Enterprise data infrastructure, data preparation for AI/ML, storage and query optimization, synthetic data and privacy-preserving data tooling.
2019
Software Development
45000000
Series A announced June 8, 2023; investors include NEA and Bain Capital Ventures among others.
“Backed by institutional VCs including New Enterprise Associates (NEA) and Bain Capital Ventures”
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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
What You’ll Bring
Bonus
Compensation & Benefits
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
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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