Senior Scientist, AI-Enabled De Novo Antibody Discovery, Protein Therapeutics
Gilead Sciences
Summary
The Senior Scientist, AI-Enabled De Novo Antibody Discovery, Protein Therapeutics will help advance AI-enabled de novo antibody discovery through iterative design-build-test-learn cycles. This 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. You 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.
Job description
Key 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.
- Critically 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.
Basic Qualifications
- Bachelor’s Degree and 8+ years of relevant experience OR Master’s Degree and 6+ years of relevant experience OR PhD or PharmD and 2+ years of relevant experience
Preferred Qualifications
- 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.
- Demonstrated ability to independently design, lead, and execute antibody discovery campaigns in a cross-functional environment.
- Experience with conventional antibodies and VHHs.
- Strong expertise in structural biology and protein structure-function relationships, including the 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.
- Demonstrated 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 the ability to integrate computational and experimental evidence.
- Ability to collaborate effectively with machine learning scientists and protein production and screening teams.
Requirements
- Bachelor’s Degree and 8+ years of relevant experience OR Master’s Degree and 6+ years of relevant experience OR PhD or PharmD and 2+ years of relevant experience
- 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
- Demonstrated ability to independently design, lead, and execute antibody discovery campaigns in a cross-functional environment
- Experience with conventional antibodies and VHHs
- Strong expertise in structural biology and protein structure-function relationships, including the 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
- Demonstrated 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 the ability to integrate computational and experimental evidence
- Ability to collaborate effectively with machine learning scientists and protein production and screening teams
Benefits
- Medical, Dental & Vision
- Life Insurance
- Discretionary Annual Bonus
- Stock-Based Long-Term Incentives
- Paid Time Off
Company overview
Gilead Sciences, Inc. is a leading American biopharmaceutical company headquartered in Foster City, California, that specializes in researching, developing, and commercializing innovative medicines for life-threatening diseases. Founded in 1987, Gilead has grown into a global healthcare leader with approximately 17,600 employees worldwide. The company's primary therapeutic focus areas include HIV/AIDS, viral hepatitis (hepatitis B and C), oncology, inflammation, and respiratory diseases. Gilead is renowned for its groundbreaking antiviral therapies, including Sovaldi and Harvoni for hepatitis C, and its comprehensive portfolio of HIV treatments such as Truvada, Atripla, and Biktarvy. The company also developed Veklury (remdesivir) for COVID-19 treatment. Gilead operates as a publicly traded company on the NASDAQ (GILD) and is a component of both the Nasdaq-100 and S&P 100 indices. Through strategic acquisitions like Kite Pharma and Immunomedics, Gilead has expanded into cell therapy and oncology, demonstrating its commitment to advancing medical science and improving patient outcomes globally.