Small molecule drug discovery is one of the most exciting open problems in machine learning. Traditional approaches require over ten years and two billion dollars to develop a new pharmaceutical, and their reliance on trial-and-error calls out for better predictive and generative models. The existent datasets are large enough to benefit from sophisticated deep learning architectures, but small enough that ML models can be trained in a few days, facilitating rapid experimentation and innovation. Nevertheless, the current industry standard has progressed little beyond shallow ML techniques such as random forests and support vector machines, largely due to the difficulty of integrating world-class machine learning research with chemistry and pharmacology expertise.

Variational AI is searching for a machine learning engineer to join us in our quest to radically accelerate the development of new drugs through machine learning excellence. For over six years, we have been  advancing the state-of-the-art , and delivering projects to  customers including Merck ,  Rakovina Therapeutics , and ImmVue Therapeutics.

You will help run experiments to evaluate new architectures and tune hyperparameters; continually improve the robustness and efficiency of our existing code base; identify, clean, prepare, and test datasets; and apply our pipeline to new targets, to help develop novel drugs. In this process, you will have the opportunity to build your skills by collaborating with our team of accomplished ML scientists and chemists. Software engineering expertise is required, as well as experience with deep learning and generative models; knowledge of chemistry and pharmacology is preferred but not required.