Prescient Design is looking for Machine Learning Engineers to develop structural and machine learning-based methods for molecular design within the companies Research and Early Development (gRED) organization, focusing on deploying new techniques for machine learning-based molecular optimization for the analysis and design of small and large molecule drugs.
Requirements
- 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).
- Molecular property prediction
- Computational chemistry
- De novo drug design
- Medicinal chemistry
- Small molecule design
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.
- Engineering pipelines for probabilistic molecular property prediction and Bayesian acquisition for active learning-based drug discovery.
- Engineering pipelines for molecular generative modeling.
- Develop machine learning and Bayesian optimization workflows to analyze existing and design new small and large molecules.
- Work on existing projects and generate new project ideas.
Other
- PhD in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics), or MS with 3+ years of industry experience.
- Record of achievement, including at least one high-impact first author publication or equivalent.
- Excellent written, visual, and oral communication and collaboration skills.
- Public portfolio of computational projects (e.g., GitHub).
- Hybrid Working Model
