Research Scientist – Tabular & Structured Machine Learning

📋 Description

• • Granica Inc. is at the forefront of revolutionizing AI by tackling the fundamental inefficiencies in data that currently limit its potential. We are seeking a highly innovative and driven Research Scientist to join our pioneering team, focusing on the critical domain of machine learning for tabular and structured data. This role is not about developing general LLM applications; instead, it's about pushing the boundaries of how machines learn from the vast, economically vital world of structured information that underpins enterprise decision-making.
• • Our mission is to eliminate data inefficiency through a unique combination of advances in information theory, probabilistic modeling, and distributed systems. We are building self-optimizing data infrastructure that continuously enhances how information is represented, compressed, and utilized by AI. The research group, led by the esteemed Prof. Andrea Montanari from Stanford, bridges cutting-edge learning theory and information efficiency with large-scale distributed systems, driven by the conviction that the next significant leap in AI will stem from more efficient learning systems and superior data representations, not just larger models.
• • While much of modern AI research is concentrated on unstructured data like text, images, or video, Granica is dedicated to the less explored but economically paramount realm of large-scale structured and tabular data. We are pioneering a new class of structured AI models – foundational models specifically designed to learn and reason from relational, tabular, and structured datasets. This is the frontier where systems will understand and reason over the structured information that powers the global economy.
• • As a Research Scientist, you will be instrumental in inventing and prototyping novel algorithms that advance the foundational principles of machine learning for structured and tabular data. Your work will involve developing innovative representation learning techniques and information models tailored for large enterprise datasets. You will build adaptive learners that synergistically combine statistical learning theory, probabilistic modeling, and large-scale systems optimization.
• • A key aspect of this role will be contributing to the development of large tabular models and structured foundation models, pushing the envelope of what's possible in this domain. You will design sophisticated architectures that integrate relational, symbolic, and neural learning components, creating a holistic approach to structured data intelligence. Furthermore, you will research and implement advanced methods for dataset compression, intelligent selection, and optimized representation to dramatically improve learning efficiency.
• • Your responsibilities will extend to developing sophisticated cost models and optimization frameworks specifically for large-scale structured learning systems, ensuring both performance and economic viability. You will collaborate closely with the Granica research group, including Prof. Andrea Montanari, and with our talented systems engineers, fostering a dynamic and interdisciplinary research environment. A crucial part of your role will be the rapid prototyping of new algorithms and their rigorous evaluation on real-world enterprise datasets, ensuring practical applicability and impact.
• • We encourage and expect contributions to the broader research community through publications and presentations, helping to shape the future of structured AI and efficient ML systems. This is an opportunity to work on challenging problems at the intersection of theory and practice, with the potential to make a significant impact on the future of AI and data infrastructure. You will be empowered to explore novel ideas, translate theoretical concepts into tangible prototypes, and contribute to a groundbreaking mission.