We are seeking an innovative and highly experienced scientist to lead the application of artificial intelligence and machine learning to the design of next-generation antibody-drug conjugates (ADCs). This role will integrate computational biology, antibody engineering, linker-payload chemistry, and translational drug discovery to create ADCs with improved efficacy, safety, and manufacturability.

The successful candidate will collaborate across biologics, medicinal chemistry, data science, and translational research teams to design and optimize antibody architectures, conjugation strategies, and linker systems for oncology, immunology, and emerging therapeutic areas.

Key Responsibilities

• Develop and implement AI/ML models to guide ADC discovery and optimization, including target selection, antibody engineering, linker design, and payload selection.
• Build predictive models for critical ADC properties, including stability, internalization, efficacy, pharmacokinetics, toxicity, and therapeutic index.
• Apply generative AI, protein language models, and structure-based design approaches to create novel antibody formats and conjugation strategies.
• Integrate sequence, structural, biophysical, chemical, and biological datasets to accelerate iterative design-build-test cycles.
• Inform engineering of monoclonal antibodies and advanced biologic formats to improve affinity, selectivity, internalization, developability, and manufacturability.
• Design strategies for site-specific conjugation and optimized drug-to-antibody ratio (DAR).
• Guide experimental validation of engineered antibodies through close partnership with discovery and characterization teams.
• Develop predictive frameworks connecting linker chemistry and conjugation properties to efficacy, safety, biodistribution, and resistance mechanisms.
• Partner with chemistry teams to advance novel conjugation technologies and next-generation payload delivery platforms.
• Establish data strategies and computational workflows that improve decision quality and reduce experimental cycle times.
• Communicate findings and recommendations to project teams, senior leadership, and external scientific audiences.
• Maintain awareness of advances in AI, protein engineering, linker-payload technology, conjugation chemistry, and computational drug discovery.