Diffuse Bio

The role:
<ul>
<li>Extend and scale Diffuse's in-house deep generative modeling toolkit for 
   downstream applications in molecular design.</li>
<li>Thoughtfully execute deep learning experiments to improve performance 
   of models or develop new functionality (e.g. loop engineering, 
   structure prediction of protein-protein complexes).</li>
<li>Work closely with software engineers to build systems for efficient 
   training and deployment of deep learning models.</li>
</ul>
Ideal background:
<ul>
<li>Self-starter who enjoys working on tough scientific problems and is results-driven.</li>
<li>Able to think critically, methodically, and creatively about experiments.</li>
<li>Proficient in Python.</li>
<li>Experience working with deep learning frameworks (e.g., PyTorch).</li>
<li>3+ years of industry experience in a data science or engineering position.</li>
<li>Track record of impressive work in industry/academia centered on ML / deep learning.</li>
<li>Graduate degree in math, CS, stats, bioengineering, comp bio, or a related field (not a hard requirement for exceptional candidates).</li>
<li>Is located in the Bay Area (remote work is an option for exceptional candidates).</li>
</ul>
Pluses:
<ul>
<li>Knowledge of physics, math, molecular biology, chemistry, etc.</li>
<li>Previous work on ML applied to problems in structural biology or molecular design.</li>
<li>Strong publication record.</li>
</ul>
What we offer:
<ul>
<li>The opportunity to join the founding team and play a critical and expanding role in shaping the company.</li>
<li>The opportunity to work on cutting-edge AI with leading researchers from top institutions.</li>
</ul>