Machine Learning Engineer

4 days ago

South San Francisco, California, United States Pharmaceutical Company Full time $200,000 - $250,000 per year

Machine Learning Engineer

Hybrid Working Model - Need Local Candidate only

We are looking for talented Machine Learning Engineers to join Prescient Design, a division devoted to developing structural and machine learning-based methods for molecular design.

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.

The Role:

  • Join Prescient Design within the Computational Sciences organization in Research and Early Development.
  • Collaborate closely with scientists within Prescient and across Research and Early Development.
  • Develop machine learning and Bayesian optimization workflows to analyze existing and design new small and large molecules.
  • Form close working relationships with small molecule and protein therapeutic development efforts across Research and Early Development.
  • Work on existing projects and generate new project ideas.

Qualifications:

  • 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).
  • Record of achievement, including at least one high-impact first author publication or equivalent.
  • Excellent written, visual, and oral communication and collaboration skills.

Additional Desired Qualifications

  • Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit).

Previous focus on one or more of the following:

  • 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 (e.g., GitHub).

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