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).
- Have ownership of a scoped-out project centered on advancing internal methods.
- Located in the Bay Area (remote work is an option for exceptional candidates).
- 4 month minimum (3 months for exceptional candidates).
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).
- Track record of impressive work in industry/academia centered on ML / deep learning.
- Currently pursuing a graduate degree in math, CS, stats, bioengineering, comp bio, or a related field (not a hard requirement for exceptional candidates).
- Industry experience in a data science or engineering position
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 work on cutting-edge AI with leading researchers from top institutions.
