Senior Scientist, Machine Learning (Biologics Design)

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. 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 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 Required Qualifications 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 Preferred Qualifications 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 Compensation 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*. For additional benefits information, visit: * Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans. Equal Employment Opportunity For jobs in the United States: 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 mental disability, genetic information or characteristic, gender identity and expression, veteran status, or other non‑job related characteristics or other prohibited grounds specified in applicable federal, state and local laws. In order to ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Era Veterans’ Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990, applicants who require accommodation in the job application process may contact View email address on click.appcast.io for assistance. For more information about equal employment opportunity protections, please view the Know Your Rights poster.

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