ML Research Engineer

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.

See the posting on Diffuse Bio's site