ML Research Intern

Diffuse Bio•Redwood City, CA

1d•Hybrid

About The Position

At Diffuse Bio, we are building generative AI for protein therapeutics. Our team has been instrumental in AI protein design breakthroughs for the past 6 years, including the first experimental validation of AI-generated proteins and diffusion models for protein structure and sequence. We have developed the first fully AI-driven computational nanobody design platform and are currently testing designs in the lab. Our objective is to create AI systems capable of designing protein therapeutics for the most difficult and high-value targets, recognizing the immense potential of AI to transform drug discovery and broaden the range of treatable diseases. We are seeking candidates eager to join our founding team and contribute to the company's growth.

Requirements

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.

Nice To Haves

Self-starter who enjoys working on tough scientific problems and is results-driven.
Able to think critically, methodically, and creatively about experiments.
Knowledge of physics, math, molecular biology, chemistry, etc.
Previous work on ML applied to problems in structural biology or molecular design.
Strong publication record.

Responsibilities

Extend and scale Diffuse’s in-house deep generative modeling toolkit for downstream applications in molecular design.
Thoughtfully execute deep learning experiments to improve model performance 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.