Senior Scientist: AI-Driven Antibody Design & Engineering

Astellas; South San Francisco (CA); Hybrid; USD 119,000 - 170,000; Full time

Benefits offered by this job

Medical, Dental and Vision Insurance

Generous Paid Time Off (Vacation, Sick

National holidays & year-end shut down

401(k) match and company contributions

Annual Corporate Bonus and Quarterly 0

Long Term Incentive Plan

Company fleet vehicle

Job summary

Astellas is seeking a Senior Scientist, Antibody Engineering in SSF to lead AI/ML-driven de novo protein/antibody design within the SSF Antibody Engineering team. The role bridges computational and experimental sciences to accelerate candidate discovery from design through to the clinic.

You will apply Python programming, AI/ML tools, and structural biology knowledge to design constructs, run assays (BLI, ELISA) and integrate data to refine models, while collaborating with CROs and partners.

Qualifications

• PhD with 5+ years related industry and/or post-doctoral experience in Biochemistry, Biophysics, Computational Biology and Structural Biology.
• Proficiency in Python programming 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, structural biology tools (PyMOL, Boltz, AlphaFold, Rosetta, etc.)
• Familiarity with cloud computing, database management, and data engineering pipelines.
• Familiarity with different antibody formats, antibody-drug assessment and validation process
• 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, with the ability to clearly articulate computational and experimental strategies, present complex data, and communicate recommendations to diverse audiences

Responsibilities

• Develop and expand SSF Antibody Engineering Team capability in AI/ML-based de novo protein/antibody design and engineering.
• Participate in relevant Program Teams, understand the biology behind programs for the designs of antibodies, bispecific antibodies, T-cell engagers (TCEs), antibody-drug conjugates (ADCs), engineered cytokines, and other biologic therapeutics etc.
• 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/cytotoxicity assays and developability assessment to support molecule selection and optimization
• Integrate experimental data to improve AI/ML models and develop modality-specific AI/ML protein engineering platforms
• Support coordination with CROs and external partners for protein production, analytical characterization, assay execution, and data review
• Contribute to biologics data management system, ELN, technical reports, documentation, invention disclosures, patent applications, and external presentations when appropriate
• Proactively maintain Astellas network/communication of experts in biologics, small molecules, cell and gene therapy and digital / IT / AI drug discovery teams

Skills

Python

Generative AI

Machine learning

Cloud computing

Data pipelines

Antibody formats

Communication

Education

PhD in Biochemistry/Computational Biology/Structural Biology

Tools

PyMOL

Rosetta

AlphaFold

Boltz

Job description

Astellas is seeking a Senior Scientist, Antibody Engineering in SSF to lead AI/ML-driven de novo protein/antibody design within the SSF Antibody Engineering team. The role bridges computational and experimental sciences to accelerate candidate discovery from design through to the clinic.

You will apply Python programming, AI/ML tools, and structural biology knowledge to design constructs, run assays (BLI, ELISA) and integrate data to refine models, while collaborating with CROs and partners.