Research Scientist – Large Tabular Models (LTMs)

The role

You will develop new machine learning algorithms for Large Tabular Models. This involves researching methods for selecting, augmenting, and compressing training data and building representation learning techniques for structured and relational datasets.

What you will do

• 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.

Required qualifications

• 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.

Nice to have

• PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field
• Experience in structured learning, representation learning, generative modeling, probabilistic modeling, statistical learning, or scalable ML systems
• 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.

Benefits

• Competitive salary
• Meaningful equity
• Performance bonus for top performers
• 401(k) with company match
• Comprehensive health coverage
• Unlimited PTO
• Daily catered meals in our Mountain View office
• Support for research, publication, and conference participation

About the company

Granica helps enterprises run AI for less, from the data layer to agent execution. The research organization is led by Stanford Professor Andrea Montanari.