Tech Stack

Description

You will develop and deploy machine learning models for potency, selectivity, ADME, and safety endpoints to accelerate drug discovery. You'll integrate ML with molecular modeling and generative design, and partner cross-functionally to define AI/ML strategies, collaborating with IT and research teams to build scalable infrastructure.

Requirements

• PhD in Computational Chemistry, Cheminformatics, ML, or related field
• 8+ years biotech/pharma experience applying ML to drug discovery
• Deep hands-on experience with ML methods on molecular data
• Strong understanding of chemical representations, structure-property relationships, and ADME/Tox
• Proven track record of ML in molecular design and cross-functional collaboration

Responsibilities

• Develop and deploy ML models for potency, selectivity, ADME, and safety endpoints
• Integrate ML with molecular modeling and generative design for molecular optimization
• Partner cross-functionally to define and execute AI/ML strategies for DMTA cycle acceleration
• Collaborate with IT and Research on data pipelines and molecular modeling infrastructure
• Advance computational capabilities by evaluating novel AI and modeling technologies