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.
