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
  machine
  learning 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

hr@dawarconsulting.com
/
kavitha@dawarconsulting.com