Machine Learning Engineer

Dawar Consulting•South San Francisco, CA

14d

About The Position

The successful candidate will manage projects deploying new techniques for machine learning based molecular optimization for the analysis and design of small and large molecule drugs within target -driven design campaigns. Special focus will be given to engineering pipelines for probabilistic molecular property prediction and Bayesian acquisition for active learning based drug discovery. Additional activities may extend to include engineering pipelines for molecular generative modeling You will join Prescient Design within the Computational Sciences organization in gRED. Your peers will be machine learning scientists, engineers, computational chemists, and computational biologists. You will closely collaborate with scientists within Prescient and across gRED. You will develop machine learning and Bayesian optimization workflows to analyze existing, and design new, small and large molecules.

Requirements

Demonstrated experience with machinelearning libraries in production -ready workflows (e.g., PyTorch + Lightning + Weights and Biases)
Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit)
Previous focus on one or more of these areas: molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self -supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, statistical methods.
Public portfolio of computational projects (available on e.g. GitHub).
PhD degree in a quantitative field (Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics), or MS degree and 3+ years of industry experience.