Granica
Research Scientist – Large Tabular Models (LTMs)
1w ago
160000 –250000 USD / yearUSASeniormachine learningrepresentation learninggenerative modelingdata augmentationdata compressionbenchmarking
Develop new machine learning algorithms and research methods for Large Tabular Models focusing on generative AI from structured enterprise data.
Requirements
- 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.
Nice to have
- 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.
Conditions
- 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 .
Other
- 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:
- 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.
- 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.