Senior Scientist, Machine Learning (Biologics Design)

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Job Description

Gileads Research Data Sciences is seeking aSenior Scientistto develop and apply machine learning methods for the design and optimization of largemolecules including antibodiesmultispecifics and other complex formats. This role sits at the intersection of machine learning structural biophysics and protein therapeutics withdirectimpact 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 learningusing limited and noisy experimental data to make high-confidence design decisions.

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 approachesrelevant to biologics including protein language modelsgeometric deep learning and generative methods (e.g. diffusion inverse foldingProteinMPNN-style approaches)
• Performstructure-based modeling and analysisof antibodiesandmultispecifics.
• Partner closely with protein therapeutics structural biology assay and engineering teams totranslate computational results into experimental decisions

Required Qualifications

• PhDin Computational Biology Computer Science Mathematics Physics Chemistry Bioengineering or a related quantitative discipline and 2 years of experience
• Strongproficiencyin Python and deep learning frameworks such asPyTorch(and/or JAX) plus standard scientific libraries (NumPy pandas etc.)
• Demonstrated experiencearchitecting training and evaluating deep learning models such as representation learning multimodal learning geometric deep learning or generative modeling
• Solid understanding ofprotein structure antibody architecture and biophysical principlesrelevant to large-molecule therapeutics
• Demonstrated research productivity (-author publications)and ability to communicate clearly to diverse audiences

Preferred Qualifications

• Experience with molecular modeling or simulations ( 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

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