Machine Learning Engineer managing projects deploying techniques for molecular optimization in diagnostics and life sciences. Focused on engineering pipelines for probabilistic predictions and Bayesian acquisition.
Responsibilities
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.
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