Research Scientist – Tabular & Structured Machine Learning

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.

• 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

• 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!