VANCOUVER, BC (OR REMOTE*) / 2+ YRS PROFESSIONAL EXPERIENCE
You will help design, implement, test, and refine novel elements of a machine learning architecture built from the ground up to optimize the properties of small molecule drugs; continually improve the robustness of our existing code base; and apply our pipeline to new drug targets. Experience developing novel ML algorithms in domains such as diffusion models, Transformers, graph neural networks, uncertainty quantification, and Bayesian optimization is required, but we can provide all necessary background in chemistry, pharmacology, and biology.
Here is the background we're looking for:
- Ph.D. in CS, applied mathematics, statistics, physics, or related discipline;
- Expertise with machine learning techniques, including diffusion models, Transformers, and Bayesian optimization, demonstrated through first-author publications in conferences like NeurIPS, ICLR, and ICML;
- Two or more years' experience developing robust code on larger projects, including code review, refactoring, unit testing, version control, etc.;
- Mastery of Python and PyTorch; and
- Intellectual curiosity and drive to excel.
Compensation is a competitive mix of cash and options. We prioritize expertise and passion over where you decide to live and work; however, for collaboration across our team, applicants must be based in *North American time zones.
To learn more about us, you can find some of our recent work at variationalai.substack.com
