Senior Scientist, Machine Learning (Biologics Design)

Gilead Sciences•Foster City, CA

22h

About The Position

At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference. Every member of Gilead’s team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact. We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together. 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. This role sits at the intersection of machine learning, structural biophysics, and protein therapeutics, with direct impact on lead optimization and pipeline programs. You will build predictive and generative models that guide sequence and structure design, integrate diverse experimental and structural datasets, and work in close partnership with experimental teams. A key emphasis is data-efficient learning, using limited and noisy experimental data to make high-confidence design decisions.

Requirements

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, such as 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

Nice To Haves

Experience with molecular modeling or simulations (e.g., 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

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