About the role

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).

Pluses:

• 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.

About Diffuse Bio

At Diffuse Bio we’re building a push-button entirely AI software platform for drug design, leveraging breakthroughs in generative AI. Our team has been behind breakthroughs in AI protein design for the past 6 years, including the first experimental validation of AI-generated proteins and diffusion models for protein structure and sequence.