Granica
Research Scientist – Diffusion Models
1w ago
USASeniorRemotemachine learningdiffusion modelsgenerative modelsrepresentation learning
Research and develop novel diffusion models and generative learning algorithms for enterprise data applications.
Requirements
- PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field.
- Strong research record in generative machine learning.
- Experience developing new generative 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 with diffusion models, score-based generative modeling, representation learning, probabilistic modeling, or scalable ML systems is particularly relevant.
Nice to have
- Research applying diffusion models beyond traditional vision tasks.
- Publications at NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, 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
- Diffusion models have transformed image, video, and multimodal AI.
- We're applying those ideas to one of the next frontiers in machine learning.
- At Granica, we're building Large Tabular Models (LTMs) —foundation models designed to learn natively from enterprise data. Realizing that vision requires new generative modeling techniques capable of learning from structured information at scale.
- Our research is led by Prof. Andrea Montanari (Stanford) and explores a fundamental question:
- If you're excited about inventing new generative learning algorithms and applying them to entirely new domains, we'd love to talk.
- Develop novel diffusion models and generative learning algorithms.
- Research new representation learning techniques for Large Tabular Models.
- Design efficient training methods for large-scale generative models.
- Prototype and evaluate new generative modeling approaches.
- Design rigorous experiments and benchmarks to measure model quality and efficiency.
- Collaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems.