Machine Learning Engineer

Our client, a world leader in diagnostics and life sciences, is looking for a "Machine Learning Engineer” based out of South San Francisco, CA.

Job Duration: Long Term Contract (Possibility Of Further Extension)

Company Benefits: Medical, Dental, Vision, Paid Sick leave, 401K

Job Description:

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. 
  

• 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).
  

Qualifications:

If interested, please send us your updated resume at

[email protected]/[email protected]