Senior Scientist, Machine Learning (Biologics Design)
Gilead Sciences
5 months ago
Remote friendly (San Francisco Bay Area)
United States
Clinical Research and Development
Senior Scientist (Machine Learning for Large Molecule Design)
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 (e.g., active learning, Bayesian optimization, experimental design) to prioritize designs and guide iterative experimentation
- Apply and extend modern deep learning approaches relevant to biologics, including protein language models, geometric deep learning, and generative methods (e.g., diffusion, inverse folding, ProteinMPNN-style approaches)
- Perform structure-based modeling and analysis of antibodies and multispecifics
- Partner closely with protein therapeutics, structural biology, assay, and engineering teams to translate computational results into experimental decisions
Required Qualifications
- PhD in Computational Biology, Computer Science, Mathematics, Physics, Chemistry, Bioengineering, or a related quantitative discipline, and 2+ years of experience
- Strong proficiency in Python and deep learning frameworks such as PyTorch (and/or JAX), plus standard scientific libraries (NumPy, pandas, etc.)
- Demonstrated experience architecting, training, and evaluating deep learning models (representation learning, multimodal learning, geometric deep learning, or generative modeling)
- Solid understanding of protein structure, antibody architecture, and biophysical principles relevant to large-molecule therapeutics
- Demonstrated research productivity (e.g., first-author publications) and ability to communicate clearly to diverse audiences
Preferred Qualifications
- Experience with molecular modeling or simulations (Amber, OpenMM, Rosetta, CHARMM, coarse-grained or multi-scale methods)
- Experience developing production-grade ML tooling (experiment tracking, model registries, CI/testing, containerization, workflow orchestration)
- Prior industry experience in biologics discovery, protein engineering, or therapeutic protein development
Compensation & Benefits
- Salary range: $169,320.00 - $219,120.00
- May be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives, paid time off, and benefits including company-sponsored medical, dental, vision, and life insurance
Application Instructions
- For current employees and contractors: apply via the Internal Career Opportunities portal in Workday
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 (e.g., active learning, Bayesian optimization, experimental design) to prioritize designs and guide iterative experimentation
- Apply and extend modern deep learning approaches relevant to biologics, including protein language models, geometric deep learning, and generative methods (e.g., diffusion, inverse folding, ProteinMPNN-style approaches)
- Perform structure-based modeling and analysis of antibodies and multispecifics
- Partner closely with protein therapeutics, structural biology, assay, and engineering teams to translate computational results into experimental decisions
Required Qualifications
- PhD in Computational Biology, Computer Science, Mathematics, Physics, Chemistry, Bioengineering, or a related quantitative discipline, and 2+ years of experience
- Strong proficiency in Python and deep learning frameworks such as PyTorch (and/or JAX), plus standard scientific libraries (NumPy, pandas, etc.)
- Demonstrated experience architecting, training, and evaluating deep learning models (representation learning, multimodal learning, geometric deep learning, or generative modeling)
- Solid understanding of protein structure, antibody architecture, and biophysical principles relevant to large-molecule therapeutics
- Demonstrated research productivity (e.g., first-author publications) and ability to communicate clearly to diverse audiences
Preferred Qualifications
- Experience with molecular modeling or simulations (Amber, OpenMM, Rosetta, CHARMM, coarse-grained or multi-scale methods)
- Experience developing production-grade ML tooling (experiment tracking, model registries, CI/testing, containerization, workflow orchestration)
- Prior industry experience in biologics discovery, protein engineering, or therapeutic protein development
Compensation & Benefits
- Salary range: $169,320.00 - $219,120.00
- May be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives, paid time off, and benefits including company-sponsored medical, dental, vision, and life insurance
Application Instructions
- For current employees and contractors: apply via the Internal Career Opportunities portal in Workday