ML Engineering Director, AI for Drug Discovery

Technical Leadership and Strategy

Own technical strategy and roadmap for the AI4DD engineering function, ensuring alignment with the overall research goals and pharmaceutical portfolio needs.

Lead the ML Engineering and Infrastructure team designing, and maintaining a robust, scalable, and secure ML platforms to support machine learning model training, experimentation, deployment, and inference for frontier research.

Align closely with other related CSCoE departments across the whole system and data value chain.

Partner closely with our LLM and Agent research teams to optimize infrastructure for foundation model training and the orchestration of autonomous agentic workflows.

Partner closely with our Large and Small Molecule Portfolio teams to ensure data infrastructure, resource, and product alignment for maximum acceleration of the drug discovery pipeline.

Oversee the Data Engineering team's efforts in the architecture and development of data pipelines and data products, ensuring high-quality, traceable, and accessible data for advanced early phase R&D activities.

Direct the Product Engineering team’s development of researcher-facing applications and tools, such as protein design interfaces and predictive modeling platforms.

Team Management and Development

Lead, mentor, and grow a diverse team of machine learning, software, and data engineers as well as developers.

Foster a culture of technical excellence, continuous integration, robust testing, and collaborative problem-solving.

Manage project execution, resource allocation, and budget planning for all engineering initiatives.

Cross-Functional Collaboration

Work closely with AI/ML Research Scientists, Computational Biologists, and therapeutic areas leads to translate research prototypes into production-ready systems and tools.

Collaborate with IT and security teams to ensure engineering practices adhere to all necessary enterprise standards.