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.
Focus on 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.
Requirements
PhD in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics), or MS with 3+ years of industry experience.
Demonstrated experience with machine learning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases).
Significant experience in at least one of the following areas: Molecular property prediction, Probabilistic modeling/inference, Bayesian optimization or active learning, Production software engineering or pipeline optimization, Cheminformatics.
Strong software engineering experience is required.
Benefits
Medical
Dental
Vision
Paid Sick leave
401K
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