Senior Scientist, Machine Learning (Biologics Design)
Gilead Sciences
June 30, 2026
Remote friendly (San Francisco Bay Area)
United States
Clinical Research and Development
Job Description
Gilead’s Research Data Sciences is seeking a Senior Scientist to develop and apply machine learning methods for the design and optimization of large molecules, including antibodies, multispecifics, and other complex formats, with direct impact on lead optimization and pipeline programs.
Key Responsibilities
- Develop and apply ML models for biologics design, including sequence-to-function, structure-aware, and multi-objective models that support lead optimization decisions
- Implement data-efficient modeling strategies (active learning, Bayesian optimization, experimental design) to prioritize designs and guide iterative experimentation
- Apply and extend modern deep learning approaches for biologics (protein language models, geometric deep learning, generative methods such as diffusion, inverse folding, ProteinMPNN-style approaches)
- Perform structure-based modeling and analysis of antibodies and multispecifics
- Partner with protein therapeutics, structural biology, assay, and engineering teams to translate computational results into experimental decisions
Required Qualifications
- PhD in a quantitative discipline (Computational Biology, Computer Science, Mathematics, Physics, Chemistry, Bioengineering, or related) and 2+ years of experience
- Proficiency in Python and deep learning frameworks (PyTorch and/or JAX) plus NumPy, pandas, etc.
- Experience architecting, training, and evaluating deep learning models (representation, multimodal, geometric deep learning, generative)
- Solid understanding of protein structure, antibody architecture, and relevant biophysical principles
- Research productivity (e.g., first-author publications) and clear communication
Preferred Qualifications
- Molecular modeling/simulations (Amber, OpenMM, Rosetta, CHARMM, coarse-grained or multi-scale methods)
- Production-grade ML tooling (experiment tracking, model registries, CI/testing, containerization, workflow orchestration)
- Industry experience in biologics discovery, protein engineering, or therapeutic protein development
Compensation & Benefits (if eligible)
- Salary range: $169,320.00 - $219,120.00; may include discretionary annual bonus, discretionary stock-based long-term incentives, paid time off, and company-sponsored medical, dental, vision, and life insurance.
Application Instructions
- Apply via the Internal Career Opportunities portal in Workday (for current employees and contractors).
Gilead’s Research Data Sciences is seeking a Senior Scientist to develop and apply machine learning methods for the design and optimization of large molecules, including antibodies, multispecifics, and other complex formats, with direct impact on lead optimization and pipeline programs.
Key Responsibilities
- Develop and apply ML models for biologics design, including sequence-to-function, structure-aware, and multi-objective models that support lead optimization decisions
- Implement data-efficient modeling strategies (active learning, Bayesian optimization, experimental design) to prioritize designs and guide iterative experimentation
- Apply and extend modern deep learning approaches for biologics (protein language models, geometric deep learning, generative methods such as diffusion, inverse folding, ProteinMPNN-style approaches)
- Perform structure-based modeling and analysis of antibodies and multispecifics
- Partner with protein therapeutics, structural biology, assay, and engineering teams to translate computational results into experimental decisions
Required Qualifications
- PhD in a quantitative discipline (Computational Biology, Computer Science, Mathematics, Physics, Chemistry, Bioengineering, or related) and 2+ years of experience
- Proficiency in Python and deep learning frameworks (PyTorch and/or JAX) plus NumPy, pandas, etc.
- Experience architecting, training, and evaluating deep learning models (representation, multimodal, geometric deep learning, generative)
- Solid understanding of protein structure, antibody architecture, and relevant biophysical principles
- Research productivity (e.g., first-author publications) and clear communication
Preferred Qualifications
- Molecular modeling/simulations (Amber, OpenMM, Rosetta, CHARMM, coarse-grained or multi-scale methods)
- Production-grade ML tooling (experiment tracking, model registries, CI/testing, containerization, workflow orchestration)
- Industry experience in biologics discovery, protein engineering, or therapeutic protein development
Compensation & Benefits (if eligible)
- Salary range: $169,320.00 - $219,120.00; may include discretionary annual bonus, discretionary stock-based long-term incentives, paid time off, and company-sponsored medical, dental, vision, and life insurance.
Application Instructions
- Apply via the Internal Career Opportunities portal in Workday (for current employees and contractors).