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GranicaGranicaMountain View, CA

Research Scientist

Research Scientist developing novel diffusion models and generative algorithms for Large Tabular Models (LTMs) on enterprise data. Requires PhD and strong track record in generative ML; experience with diffusion models and PyTorch/JAX preferred.

Salary not listed
HybridAI Research

About the role

What You'll Work On

  • 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.

What We're Looking For

  • 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.

Bonus

  • 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.

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.

Skills

Diffusion ModelsGenerative ModelsPyTorchJAXPythonRepresentation LearningProbabilistic ModelingScalable Ml

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