ML Research Intern

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.

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.