Senior Scientist, AI/ML (Biologics Design)

Gilead Sciences • Foster City, CA

7d • $169,320 - $219,120

About The Position

Gilead is seeking a highly motivated computational scientist to advance machine learning methods for designing and optimizing biologic therapeutics, including antibodies, multispecifics, and emerging protein modalities. The role develops and applies modern AI approaches across protein language models, structural learning, generative modeling, and multimodal data integration.

Working at the interface of machine learning, protein engineering, and drug discovery, the Senior Scientist will build models to guide molecule design, improve developability, and accelerate candidate optimization. The role collaborates with multidisciplinary research teams and contributes to the scientific direction of AI-enabled biologics design within Research Data Sciences.

Requirements

• 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 machine learning 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.

Nice To Haves

• Experience 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.

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 sequence, structure, biophysical characterization, developability assessment, and functional screening data.
• Implement active learning and data-efficient modeling strategies for discovery programs with limited experimental data.
• Collaborate with protein engineers, structural biologists, assay scientists, and computational researchers to inform project decisions and accelerate molecule optimization.
• Contribute to the scientific direction of AI-enabled biologics design within Research Data Sciences.

Benefits

• May be eligible for a discretionary annual bonus.
• May be eligible for discretionary stock-based long-term incentives; eligibility may vary by role.
• Paid time off.
• Company-sponsored medical, dental, vision, and life insurance plans.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree