Research Scientist – Large Tabular Models (LTMs)
Role Overview
This senior-level Research Scientist role involves inventing algorithms and developing adaptive learners for structured AI, focusing on representation learning and efficient information modeling at petabyte scale. You will design architectures integrating symbolic, relational, and neural components, collaborate with the Granica Research group led by Prof. Andrea Montanari, and transform theoretical ideas into production-grade systems for enterprise workloads. The impact includes advancing foundational models for structured data and contributing to global research on efficient learning.
Perks & Benefits
The role offers a competitive salary, meaningful equity, and substantial bonuses, with comprehensive health coverage for you and your family, plus flexible time off. It supports research, publication, and deep technical exploration in a high-trust, low-bureaucracy environment, emphasizing real ownership and long-term impact. As a remote position, it likely allows flexible scheduling, though time zone expectations may align with team collaboration in Mountain View, CA, fostering career growth through fundamental research and enterprise impact.
Full Job Description
Location: Mountain View, CA (On-site)
Overview
Most of today's generative AI is built for text, images, and video.
Enterprise data isn't.
The world's most valuable data lives in tables: customer records, transactions, financial systems, telemetry, operational data, and business workflows. Today's generative AI stack wasn't designed to learn efficiently from this kind of information.
At Granica, we're building Large Tabular Models (LTMs)—foundation models that learn natively from structured and relational enterprise data.
Our research is led by Prof. Andrea Montanari (Stanford) and focuses on one central question:
How can we build generative AI that learns efficiently from tabular data?
That requires solving problems well beyond model architecture, including intelligent data selection, dataset augmentation, representation learning, and information-preserving compression.
If you're excited about inventing the algorithms that make Large Tabular Models possible, we'd love to talk.
What You'll Work On
Develop new machine learning algorithms for Large Tabular Models.
Research methods for selecting, augmenting, and compressing training data without losing information.
Build representation learning techniques for structured and relational datasets.
Prototype and evaluate new approaches for generative modeling over enterprise data.
Design rigorous experiments and benchmarks to measure progress.
Collaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems.
What We're Looking For
PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field.
Strong research record in machine learning.
Experience developing new models or learning algorithms.
Hands-on experience with PyTorch or JAX.
Strong programming skills in Python.
Ability to turn research ideas into working systems.
Experience in structured learning, representation learning, generative modeling, probabilistic modeling, statistical learning, or scalable ML systems is particularly relevant.
Bonus
Research on tabular, relational, or graph data.
Experience with diffusion or other generative modeling approaches.
Publications at NeurIPS, ICML, ICLR, COLT, KDD, or related venues.
Open-source or production ML systems experience.
Compensation & Benefits
Competitive salary, meaningful equity, and performance bonus for top performers
401(k) with company match, comprehensive health coverage, and unlimited PTO
Daily catered meals in our Mountain View office
Support for research, publication, and conference participation
At Granica, you'll help build the next generation of enterprise AI—from exabyte-scale data infrastructure, Large Tabular Models (LTMs), and stateful AI agents. Together, we're creating the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.
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