Senior Scientist, Machine Learning; Biologics Design

Job Description

Senior Scientist, Machine Learning for Biologics Design

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.

• 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 develop ability
• 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

• 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 (Num Py, 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

• 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 develop ability 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

The salary range for this position is: $ - $. Gilead considers a variety of factors when determining base compensation,…