Machine Learning Engineer
- Published on
About the Role
Small molecule drug discovery is a huge challenge in machine learning. Traditional methods take over ten years and two billion dollars to develop a new pharmaceutical. We aim to innovate these methods by leveraging better predictive models and generative architectures. You will help identify, clean, prepare, and test datasets; run experiments to evaluate new architectures and tune hyperparameters; and improve the robustness and efficiency of our code base.
About the Candidate
We are looking for someone with the following qualifications:
- M.S. (Ph.D. preferred) in CS, applied mathematics, statistics, physics, or a related discipline.
- Experience with machine learning techniques such as diffusion models, Transformers, and Bayesian optimization, ideally shown through publications.
- Two or more years developing robust code on larger projects with practice in code review, refactoring, unit testing, and version control.
- Mastery of Python and PyTorch.
- A solid background in chemistry or pharmacology is preferred but not mandatory.
- Intellectual curiosity and drive to excel are essential.
About the Company
Variational AI is focused on improving drug discovery through machine learning. We have been working in this field for over five years, collaborating with notable customers like Merck and Rakovina Therapeutics. Our goal is to transform the current industry standards using cutting-edge machine learning research alongside chemistry and pharmacology expertise.
Company Culture and Benefits
We are an equal opportunity employer and strongly encourage applications from women, BIPOC, and underrepresented communities. We believe that expertise and passion can thrive regardless of where you choose to live. Compensation includes a competitive mix of cash and options.
How to Apply
Please submit your resumes/CVs to infovariational.ai. To learn more about us, visit variational.ai and check out our recent developments at variationalai.substack.com.