Research Scientist – Tabular & Structured Machine Learning

Granica is focused on removing inefficiencies in AI data processing to enhance model performance. The Research Scientist role involves inventing algorithms and developing techniques for structured and tabular data, contributing to foundational models and collaborating with a leading research group.

Responsibilities:

• 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