Principal Scientist II, AI-Guided Antibody Drug Conjugate Design

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. Minimum Qualifications: • Bachelor’s Degree in Science related field with 16 years of related work; Master’s Degree Science or PharmD with 14 years of related work; PhD in Science or related field with 8 years of related work experience. • PhD in computational biology, bioengineering, protein engineering, chemistry, chemical biology, pharmaceutical sciences, or a related discipline. • 8+ years of relevant industry experience in biologics, ADCs, protein engineering, computational drug design, or related therapeutic modalities. • Demonstrated expertise in antibody engineering, including humanization, affinity maturation, format engineering, and developability optimization. • Proven track record applying machine learning, deep learning, generative AI, or computational design methods to therapeutic discovery. • Strong understanding of ADC pharmacology, intracellular trafficking, pharmacokinetics, safety drivers, and resistance mechanisms. • Experience leading or influencing multidisciplinary drug discovery teams. • Strong record of publications, patents, platform innovation, or advancement of drug discovery programs. Preferred Qualifications: • Experience with protein language models, generative molecular design, graph neural networks, multimodal learning, or biological foundation models. • Expertise in structural biology and modeling approaches, including AlphaFold-based modeling, molecular dynamics, cryo-EM, or X-ray c