Senior Scientist, AI/ML (Biologics Design) (United States - California - Foster City)

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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 ,   AI/ML  ( Biologics Design ) 

We're   seeking   a highly motivated computational scientist to advance machine learning methods for the design and optimization of biologic therapeutics, including antibodies,   multispecifics , and emerging protein modalities.  

This role is focused on the development and application of modern AI approaches spanning protein language models, structural learning, generative modeling, and multimodal data integration. You will work at the interface of machine learning, protein engineering, and drug discovery to build models that guide   molecule   design, improve developability, and accelerate candidate optimization.  

The ideal candidate combines strong machine learning   expertise   with a deep interest in protein therapeutics and a passion for translating computational innovation into experimental impact.  

Key Responsibilities  

• Develop novel machine learning approaches to support   biologics   discovery and lead optimization.  

• Apply and extend protein language models, foundation models, and   representation   learning methods for therapeutic protein design.  

• Build predictive   models   linking sequence, structure, and experimental measurements to key molecular properties.  

• Develop structure-aware learning approaches that incorporate protein conformation, interfaces, and molecular context.  

• Design and evaluate generative methods for exploring protein sequence space and proposing improved therapeutic candidates.  

• Integrate diverse data sources, including sequence, structure, biophysical characterization, developability assessments, and functional screening datasets.  

• Implement active learning and data-efficient modeling strategies for discovery programs with limited experimental data.  

• Work closely with protein engineers, structural biologists, assay scientists, and computational researchers to drive project decisions and accelerate molecule optimization.  

• Contribute to the scientific direction of AI-enabled biologics design within Research Data Sciences.  

Required Qualifications  

• Ph.D. in Computational Biology, Machine Learning, Computer Science, Structural Biology, Biophysics, Bioengineering, or a related quantitative field.  

• Demonstrated experience developing and applying machine learning methods to biological or molecular problems.  

• Strong programming skills in Python and experience with modern ML frameworks such as   PyTorch   or JAX.  

• Experience building, training, and evaluating deep learning models, including representation learning, geometric learning, multimodal learning, or generative modeling approaches.  

• Strong understanding of protein structure and sequence-function relationships.  

• Proven scientific productivity through publications, open-source contributions, or impactful research projects.  

• Excellent communication and collaboration skills within multidisciplinary scientific teams.  

Preferred Qualifications  

• Experience working with protein language models and foundation models for proteins or antibodies.  

• Familiarity with modern structure-based AI approaches, including AlphaFold-class models, inverse folding methods, geometric neural networks, or generative protein design frameworks.  

• Experience in antibody engineering,   multispecific   therapeutics, protein optimization, or   biologics   discovery.  

• Experience   modeling   developability-related properties such as stability, aggregation, solubility, viscosity, immunogenicity, or molecular liabilities.  

• Experience integrating experimental datasets with machine learning workflows to support design-build-test-learn cycles.  

• Familiarity with antibody sequence analysis, repertoire-scale datasets, or therapeutic protein databases.  

 

 

The salary range for this position is: $169,320.00 - $219,120.00. Gilead considers a variety of factors when determining base compensation, including experience, qualifications, and geographic location. These considerations mean actual compensation will vary. 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*.

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* Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans.

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Gilead Sciences Inc. is committed to providing equal employment opportunities to all employees and applicants for employment, and is dedicated to fostering an inclusive work environment comprised of diverse perspectives, backgrounds, and experiences. Employment decisions regarding recruitment and selection will be made without discrimination based on race, color, religion, national origin, sex , age, sexual orientation, physical or <]]> <