Senior Scientist, AI-Enabled De Novo Antibody Discovery, Protein Therapeutics

Gilead Sciences • Foster City, CA

22h • Onsite

About The Position

The Senior Scientist will advance AI-enabled de novo antibody discovery through iterative design-build-test-learn cycles. The role combines antibody discovery, structural biology, and computational methods to translate target protein structure, biology, and therapeutic mechanism of action into epitope selection and end-to-end discovery campaign design. The scientist will lead de novo binder design campaigns, bridge computational and experimental antibody discovery, and help establish lab-in-the-loop platforms that connect computational design, experimental screening, and iterative learning.

This position is based in Foster City, California, and is fully onsite. The role also serves as a bridge between computational and experimental antibody discovery teams and supports project teams across Gilead therapeutic areas.

Requirements

• Bachelor’s degree and 8 or more years of relevant experience, or a master’s degree and 6 or more years of relevant experience, or a PhD or PharmD and 2 or more years of relevant experience.

Nice To Haves

• PhD in structural biology, protein engineering, biochemistry, biophysics, computational protein design, or a related discipline.
• Industry experience in antibody discovery or engineering spanning target assessment through lead optimization.
• Ability to independently design, lead, and execute antibody discovery campaigns in a cross-functional environment.
• Experience with conventional antibodies and VHHs.
• Expertise in structural biology and protein structure-function relationships, including evaluation of experimental and predicted antibody-target structures.
• Experience connecting epitope selection, target biology, and therapeutic mechanism of action to antibody discovery strategies.
• Experience with antibody library design and screening technologies, including display-based or high-throughput recombinant screening.
• Hands-on experience with one or more antibody library design or screening approaches.
• Familiarity with the applications and limitations of artificial intelligence and machine learning approaches for biologics discovery and optimization.
• Familiarity with protein language models, inverse folding, structure prediction or co-folding, diffusion or flow models, and iterative data-driven lead optimization.
• Experience applying artificial intelligence or machine learning approaches to antibody discovery or optimization challenges.
• Working proficiency with Python, artificial intelligence-assisted coding tools, and Linux command-line or high-performance computing environments for scientific analysis, workflow development, and rapid prototyping.
• Strong scientific judgment and ability to integrate computational and experimental evidence.
• Ability to collaborate effectively with machine learning scientists and protein production and screening teams.

Responsibilities

• Lead de novo antibody discovery and optimization campaigns from target and epitope assessment through design, screening, iteration, and hit selection.
• Develop integrated computational and experimental screening strategies for de novo designs, including candidate prioritization, library design, multiplexing, and recombinant and display-based approaches.
• Evaluate experimental and predicted structural models to prioritize biologically relevant epitopes and guide discovery strategies.
• Integrate computational, structural, binding, functional, and developability data to inform campaign iteration and binder advancement decisions.
• Rapidly prototype computational workflows that address antibody discovery and optimization needs.
• Collaborate with machine learning scientists to refine, scale, and implement broadly useful computational solutions.
• Partner with data scientists, structural biologists, and high-throughput screening scientists to build scalable lab-in-the-loop capabilities for de novo antibody discovery and optimization.
• Serve as a bridge between computational and experimental antibody discovery teams, supporting project teams across Gilead therapeutic areas.

Benefits

• Paid time off.
• Company-sponsored medical insurance.
• Company-sponsored dental insurance.
• Company-sponsored vision insurance.
• Company-sponsored life insurance.
• May be eligible for a discretionary annual bonus.
• May be eligible for discretionary stock-based long-term incentives; eligibility may vary by role.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior