The role:

  • Extend and scale Diffuse’s in-house deep generative modeling toolkit for
       downstream applications in molecular design.
  • Thoughtfully execute deep learning experiments to improve performance
       of models or develop new functionality (e.g. loop engineering,
       structure prediction of protein-protein complexes).
  • Work closely with software engineers to build systems for efficient
       training and deployment of deep learning models.

Ideal background:

  • Self-starter who enjoys working on tough scientific problems and is results-driven.
  • Able to think critically, methodically, and creatively about experiments.
  • Proficient in Python.
  • Experience working with deep learning frameworks (e.g., PyTorch).
  • 3+ years of industry experience in a data science or engineering position.
  • Track record of impressive work in industry/academia centered on ML / deep learning.
  • Graduate degree in math, CS, stats, bioengineering, comp bio, or a related field (not a hard requirement for exceptional candidates).
  • Is located in the Bay Area (remote work is an option for exceptional candidates).


  • Knowledge of physics, math, molecular biology, chemistry, etc.
  • Previous work on ML applied to problems in structural biology or molecular design.
  • Strong publication record.

What we offer:

  • The opportunity to join the founding team and play a critical and expanding role in shaping the company.
  • The opportunity to work on cutting-edge AI with leading researchers from top institutions.

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