Senior Scientist, Ai​/Ml; Biologics Design

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

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 develop ability, 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.

• 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, develop ability 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.

• 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.

• Experience working with protein language models and foundation models for proteins or antibodies.
• Familiarity with modern structure-based AI approaches, including Alpha Fold-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 develop ability‑related properties such as stability, aggregation, solubility, viscosity,…