Principal Scientist II, AI-Guided Antibody Drug Conjugate Design
About The Position
AbbVie is seeking an innovative, highly experienced scientist to lead the application of artificial intelligence and machine learning to the design of next-generation antibody-drug conjugates (ADCs). The role integrates computational biology, antibody engineering, linker-payload chemistry, and translational drug discovery to develop ADCs with improved efficacy, safety, and manufacturability. The successful candidate will collaborate with 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. The position includes developing AI/ML approaches for ADC discovery and optimization, building predictive models, integrating complex datasets, and guiding experimental validation. The scientist will establish computational workflows and data strategies, contribute to cross-functional drug discovery programs, communicate scientific recommendations to internal and external audiences, and stay current with advances in AI and related therapeutic technologies. Travel is required 5% of the time.
Requirements
- Bachelor’s degree in a science-related field with 16 years of related work experience; a master’s degree in science or PharmD with 14 years of related work experience; or a PhD in science or a 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 or more 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.
Nice To Haves
- 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 crystallography.
- Experience developing scalable AI platforms for biologics or multimodal therapeutic design.
- Knowledge of next-generation ADC modalities, including bispecific ADCs, immune-stimulating ADCs, and targeted delivery systems.
- Demonstrated leadership of cross-functional discovery programs and scientific teams.
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 ADC properties including stability, internalization, efficacy, pharmacokinetics, toxicity, and therapeutic index.
- Apply generative AI, protein language models, and structure-based design to create novel antibody formats and conjugation strategies.
- Integrate sequence, structural, biophysical, chemical, and biological datasets to accelerate iterative design-build-test cycles.
- Inform monoclonal antibody and advanced biologic format engineering 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 in partnership with discovery and characterization teams.
- Develop predictive frameworks relating 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 to 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.
Benefits
- Paid time off, including vacation, holidays, and sick time.
- Medical, dental, and vision insurance for eligible employees.
- 401(k) for eligible employees.
- Eligibility to participate in long-term incentive programs.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
