Scientist II, Antibody Engineering

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.
• Data Presentation and Reporting: Present findings and recommendations to internal stakeholders and at scientific conferences, document computational workflows and design decisions, and contribute to publications and patent applications.

Required Qualifications

• PhD with 2+ years or MS with 7+ years of relevant experience in computational protein/antibody design, machine learning, bioinformatics, structural biology, computational biology, biophysics, or a related field.
• Demonstrated experience in computational antibody design and/or AI/ML-enabled protein design, including the use of generative and structure-prediction models (e.g., ProteinMPNN, RFdiffusion, ESMFold, IgFold, or equivalent tools).
• Experience may include target-conditioned binder design and optimization of one or more modalities such as monoclonal, bispecific, or multi-specific antibodies, TCEs, ADCs, or other engineered biologics leveraging protein language models and structural modeling tools.
• Hands-on experience in establishing, operating, or improving computational workflows for protein or antibody design.
• Demonstrated understanding of antibody discovery and engineering, including antibody sequence, structure, binding, function, format, and developability relationships.
• Ability to interpret experimental antibody data and translate the results into testable design hypotheses and molecule-prioritization decisions.
• Excellent communication skills with a demonstrated ability to articulate computational and experimental plans and present research findings and recommendations in meetings.
• Strong work ethic with a dedicated mindset of proactively identifying and addressing gaps in computational design, data, and workflow capabilities.
• Strong collaborative mindset and ability to work with global project teams, including teams in South San Francisco and Japan.
• Willingness to take on tasks and responsibilities as needed in a dynamic environment and to support multiple antibody discovery programs.

Preferred Qualifications

• Background in oncology research or strong interest in applying antibody engineering to oncology, with familiarity in AI/ML-driven tumor-targeting biologics and de novo antibody design for challenging oncology targets.
• Related industry experience in computational antibody/protein engineering, AI-based sequence generation, bioinformatics, structural biology, or molecular modeling for biologics developability.
• Experience with commercial or cloud-based platforms for AI-enabled antibody/protein design, as well as biologics data management systems, is a plus.
• Experience with antibody generation or screening technologies, such as phage display, yeast or mammalian display, B-cell discovery, immunization-based discovery, or related experimental platforms, is a plus.
• Experience coordinating work with CROs, external technology providers, or global research teams, including cross-site collaboration, is a plus.

Location and Working Environment

This position is based in SSF and will require on-site work.