The candidate will have the opportunity to develop and advance AI/ML technologies at the heart of computer-aided drug design.
The candidate should have broad experience with computational techniques with a focus on state-of-the-art methods in machine learning applied to chemical and biological data.
The key responsibilities of this position are to identify, develop, optimize, validate, and deploy innovative methods for molecular generation, drug-target interaction prediction, and modeling that leverage artificial intelligence and machine learning models to enhance molecular design capabilities.
The candidate should have a working knowledge of chemistry and the ability to work collaboratively with a broad variety of disciplines including other members of our internal computational chemistry and cheminformatics groups to accelerate drug discovery from hit identification through candidate selection.
Requirements & Skills:
BS, MS, or PhD in Chemistry/Computer Science/Machine Learning/Cheminformatics/Chemical Engineering or related education with typically 10+ years (BS), 8+ years (MS), or 0-3+ years (PhD) and experience developing machine learning models related to chemical and biological data.
Expertise in developing, implementing, and deploying programs and computational solutions employing Machine Learning/Deep Learning and Cheminformatics
Expertise in AI/ML-enabled molecular generation, pose generation, and/or affinity prediction are preferred
Ability to implement, debug, and maintain computational tools in common programming languages (Python, etc…) and proficiency with cloud computing capabilities
Strong analytical and problem-solving skills with demonstrated ability to think critically and creatively, and provide solutions both individually and collaboratively with internal experts to develop and optimize the computational discovery infrastructure including cloud resources
Excellent ability to communicate clearly and concisely with colleagues and collaborators including an ability to explain complex ideas to non-specialists
Familiarity with KNIME, Pipeline Pilot, or other automation interfaces
Familiarity with modern deep learning architectures including GNN, CNN, RNN, Transformer, GCNN, and MPNN, and machine learning paradigms such as generative models, GAN, and active learning
Experience with deep learning platforms such as Pytorch, Tensorflow, or Keras
Strong programming skills in Python and experience with data science stack including NumPy, pandas, sci-kit-learn, and other related scientific libraries
