Associate Director, AI​/ML; Biologics

Associate Director, AI/ML in Large Molecule Drug Discovery at Takeda, focusing on biologics such as antibodies, bispecifics, ADCs, and fusion proteins.

• develop and deploy advanced AI/ML models for biologics design, optimization, and develop ability engineering; integrate computational methods with experimental data; serve as a strategic AI/ML partner across discovery programs.

• PhD with 6+ years or equivalent experience in computational biology, machine learning, or related fields.
• proven expertise in ML modeling for biological data, protein structure/sequence analysis, and programming (Python).
• experience with cloud platforms and data management.

• experience with modern ML architectures ("e.g., GNNs, diffusion models"), de novo binder design, NGS data analysis, and molecular simulation.

• biologics modalities, antibody design, multi-objective optimization, integrated data cycles, AI/ML application in early drug discovery stages.

Cambridge, MA, full-time, on-site.

Working with biology, CMC, and program teams, focused on impactful AI/ML solutions in biologics discovery.