Scientist II, Antibody Engineering at Astellas Pharma in San Francisco, CA

Job description

Scientist II, Antibody Engineering About Astellas Astellas is a global life sciences company committed to turning innovative science into VALUE for patients. We provide transformative therapies in disease areas that include oncology, ophthalmology, urology, immunology and women's health. Through our research and development programs, we are pioneering new healthcare solutions for diseases with high unmet medical need. Learn more at Astellas.com. Are you driven to make a real difference in the lives of patients? We're seeking passionate individuals who thrive in dynamic environments, embrace new ideas, and aren't afraid to take intelligent risks. People who act with unwavering integrity and are deeply committed to making a tangible impact. Purpose & Scope Astellas aims to build a robust R&D portfolio by applying cutting-edge science and technology to engineered biologics, including multi-specific antibodies and antibody-drug conjugates (ADCs). We are seeking an accomplished and dynamic scientist to contribute to and lead antibody engineering projects within our global Antibody Engineering group under Biologics Engineering unit in South San Francisco. This role will support AI-enabled antibody design and data workflows across the South San Francisco and Japan sites. The scientist will establish, operate, and continuously improve scalable workflows that combine external SaaS platforms, internal computational methods, AI agents, and biologics data management to accelerate antibody discovery programs globally, while enabling broader adoption by experimental scientists over time. The primary objective of the role is to apply these capabilities, together with experimental data, to discover and engineer differentiated therapeutic antibodies and advance programs toward lead optimization and clinical candidate selection . Role and Responsibilities Lead AI-Driven De Novo Antibody Engineering Projects: Plan, design, develop, and optimize mono-, bi-, and multi-specific antibodies using generative AI and machine learning models, including computational de novo sequence generation and in silico optimization. Establish and operate scalable workflows using AI-enabled SaaS platforms, internal computational methods, and AI agents, in close collaboration with experimental teams for protein production, validation, and characterization. Use iterative design-make-test-learn cycles to support antibody discovery, lead optimization, and candidate selection Drive the dissemination and adoption of AI-enabled antibody design and related computational workflows across teams, strengthening scientific capabilities and enabling broader application of these approaches by experimental scientists. Collaborate Cross-Functionally: Work closely with cancer biology, translational science, clinical development, AI/ML, research informatics, data management, and global Biologics Engineering colleagues, including teams in South San Francisco and Japan, to advance therapeutic candidates from discovery to preclinical stages. Provide computational design and data, as well as biologics data management support for antibody engineering and discovery programs across both sites. Coordinate with CROs and external technology providers, including outsourced protein production/analytics and research collaborations. Integrate computational recommendations with binding, functional, biophysical, and developability data to inform program decisions. Innovate, Evaluate, Implement Technologies: Stay at the forefront of antibody engineering and AI/ML advancements by evaluating and integrating novel technologies, including AI-enabled SaaS platforms, AI agents, and biologics data management systems, to enhance biologics discovery and optimization. Establish reproducible and user-friendly workflows that can be scaled across programs. These technologies and workflows will serve as enablers for antibody discovery and engineering outcomes. Data Presentation and Reporting: Present findings and re