Senior Scientist, Machine Learning (Biologics Design)

Gilead Sciences•Foster City, CA

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 Senior Scientist, Machine Learning for Biologics Design Summary: Gilead’s Research Data Sciences is seeking a Senior Scientist to develop and apply machine learning methods for the design and optimization of large-molecule therapeutics, including antibodies, multispecifics, and other complex biologic 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 in a lab-in-the-loop setting. 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 OR MA//MS and 6 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, ideally using modern approaches such as representation learning, multimodal learning, geometric deep learning, or generative modeling
Experience applying ML to biological sequences and/or protein structures, with ability to translate biology/biophysics context into modeling choices and evaluation metrics
Solid understanding of protein structure, antibody architecture, and biophysical principles relevant to large-molecule therapeutics
Demonstrated research productivity (e.g., first-author publication(s) or equivalent scientific contributions), and ability to communicate clearly to diverse audiences
Ability to work independently while contributing effectively within cross-functional teams

Nice To Haves

Experience with data-limited learning and/or decision-making under uncertainty (e.g., active learning, Bayesian optimization, probabilistic modeling, or experimental design)
Experience with molecular modeling or simulations (e.g., Amber, OpenMM, Rosetta, CHARMM, coarse-grained or multi-scale methods)
Experience with complex biologic formats, including multispecifics and ADCs
Familiarity with developability considerations and metrics and how to incorporate these into multi-objective optimization
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 under limited data
Apply and extend modern deep learning approaches relevant to biologics, including protein language model embeddings, geometric deep learning (CNN/GNN), and generative methods (e.g., diffusion, inverse folding, ProteinMPNN-style approaches)
Perform structure-based modeling and analysis of antibodies, bispecifics, and other multispecific formats, including feature extraction and structure-informed interpretation of experimental outcomes
Design, evaluate, and benchmark lead optimization strategies across multiple objectives, such as affinity, specificity, stability, expression, and developability
Partner closely with protein therapeutics, structural biology, assay, and engineering teams to translate computational results into experimental decisions
Communicate findings clearly through presentations, written reports, and cross-functional discussions, with an emphasis on actionable recommendations

Benefits

This position may also be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives (eligibility may vary based on role), paid time off, and a benefits package.
Benefits include company-sponsored medical, dental, vision, and life insurance plans.
For additional benefits information, visit: https://www.gilead.com/careers/compensation-benefits-and-wellbeing
Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans.

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