Principal Machine Learning Engineer leading AI and Machine Learning systems at Bumble for recommendations and personalization. Driving improvements in user engagement and safety across Bumble products.
About the role
- 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.
Benefits
- Medical
- Dental
- Vision
- Paid Sick leave
- 401K
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