Senior Scientist, Antibody Engineering

Astellas•South San Francisco, CA

3d•Hybrid

About The Position

The Senior Scientist will lead the development of de novo protein and antibody design capabilities within Astellas’ SSF Antibody Engineering team. The role applies AI/ML approaches to optimize the Design-Make-Test-Analysis (DMTA) cycle for discovery candidates and candidate nomination, while supporting cross-functional collaboration projects. The successful candidate will connect computational and experimental sciences to help advance programs toward the clinic. The position involves developing AI/ML-based design and engineering capabilities, contributing to program teams, and applying computational and data-driven approaches to therapeutic design, sequence analysis, data interpretation, and workflow automation. The scientist will also design constructs and assays, use experimental results to improve models and engineering platforms, coordinate work with external partners, and contribute to data systems, technical documentation, intellectual property, and scientific communications. The role is based in SSF and requires on-site work, with a hybrid working solution that allows time at the office and flexibility to work from home. Hybrid work from certain locations may be permitted under Astellas’ Responsible Flexibility Guidelines.

Requirements

PhD in Biochemistry, Biophysics, Computational Biology, Structural Biology, or a related field, with 5+ years of related industry and/or postdoctoral experience.
Proficiency in Python or related scripting tools for biological data analysis, data visualization, workflow automation, and/or integration of computational and experimental datasets.
Experience with generative AI for protein design, machine learning model development, and structural biology tools such as PyMOL, Boltz, AlphaFold, or Rosetta.
Familiarity with cloud computing, database management, and data engineering pipelines.
Familiarity with different antibody formats and antibody-drug assessment and validation processes.
Good practices in data management and recording.
Strong work ethic and collaborative mindset, with demonstrated flexibility, adaptability, and willingness to support critical team priorities beyond primary project responsibilities.
Excellent written and verbal communication skills, including the ability to articulate computational and experimental strategies, present complex data, and communicate recommendations to diverse audiences.

Nice To Haves

Hands-on experience in antibody/protein engineering and characterization.
Knowledge of antibody discovery, affinity maturation, and developability assessment for biologics drug development.
Experience with NGS-based protein engineering datasets.

Responsibilities

Develop and expand the SSF Antibody Engineering Team’s capability in AI/ML-based de novo protein and antibody design and engineering.
Participate in program teams and understand program biology to design antibodies, bispecific antibodies, T-cell engagers, antibody-drug conjugates, engineered cytokines, and other biologic therapeutics.
Apply computational and data-driven approaches, including Python programming, AI/ML tools, bioinformatics, and in silico protein engineering, to support therapeutic design, sequence analysis, data interpretation, and workflow automation.
Design constructs and assays, including BLI, ELISA, cell-based binding and cytotoxicity assays, and developability assessments to support molecule selection and optimization.
Integrate experimental data to improve AI/ML models and develop modality-specific AI/ML protein engineering platforms.
Coordinate with CROs and external partners on protein production, analytical characterization, assay execution, and data review.
Contribute to biologics data management systems, ELNs, technical reports, documentation, invention disclosures, patent applications, and external presentations when appropriate.
Proactively maintain Astellas’ network and communication among experts in biologics, small molecules, cell and gene therapy, and digital/IT/AI drug discovery teams.