Senior Scientist AI/ML (Small Molecule Therapeutics)

仕事内容

Gilead has defined “Adopt and scale AI to transform how we work” as one of the company’s strategic priorities. In line with this mission, we are looking for a highly motivated team player with excellent communication skills to join our Modeling Group within Structural Biology & Chemistry located at our Foster City, CA site. The Senior Scientist AI/ML will lead the charge at the forefront of innovation by developing, evaluating and integrating cutting-edge AI/ML-based technologies. Responsibilities will include generation, validation and implementation of AI tools and ML models to increase productivity and improve efficiency of Small Molecule therapeutic projects.

This is a site based role at our global headquarters in Foster City, CA.

Specific Responsibilities:

· Partner with project teams to identify opportunities where ML models can enhance design, prioritization, & hypothesis testing across target classes and discovery stages.

· Develop, evaluate, and benchmark ML models—including geometric deep learning, generative models, and co-folding architectures—for potency, selectivity, and ADMET prediction.

· Work cross-functionally with structural and medicinal chemists to translate computational insights into clear design recommendations.

· Track model performance on active discovery programs; identify failure modes, evaluate applicability domains, and propose improvements.

· Collaborate with Research Informatics & IT teams to deploy models into scalable production environments and maintain computational workflows.

· Communicate capabilities, limitations, and key experimental insights in discovery team meetings

Essential Functions:

· Improve structure and potency prediction accuracy: Evaluate, develop, and deploy internal co-folding models on active and retrospective drug discovery programs.

· Enable virtual screening of ultra-large libraries: Assess AI/ML technologies and enhanced sampling methods on internal benchmarks; partner with modelers to apply these technologies on discovery projects.

· Bolster internal generative chemistry design for hit-to-lead and lead optimization: Evaluate multiple scoring paradigms for rapid assessment of chemical space; improve user interfaces to democratize generative workflows.

· Maintain state-of-the-art ADMET models: Train and deploy models at scale; collaborate with key stakeholders (MedChem, DMPK) to enhance adoption and analyze project-specific data.

Knowledge, Experience, and Skills:

· Strong knowledge of deep learning architectures relevant to chemistry and structural biology, including graph neural networks, geometric deep learning, diffusion or flow matching models, and multitask frameworks.

· Strong programming skills in Python and proficiency with ML frameworks (PyTorch, TensorFlow, or JAX).

· Ability to design, implement, and evaluate robust model validation strategies, including uncertainty quantification and applicability domain assessment.

· Expertise with cheminformatics toolkits such as RDKit, OpenEye, or Schrödinger.

Essential:

· PhD and 2+ years relevant research experience to the position, a proven track record of publications, or contributions to ML codebases.

· Demonstrated expertise in developing and applying ML models to real-world problems in chemistry, computational chemistry, or materials science.

· Hands-on experience with geometric deep learning, generative chemistry methods, or large‑scale molecular modeling.

Desirable:

· Background or strong interest in medicinal chemistry, ADMET modeling, or cheminformatics.

· Knowledge of small-molecule drug discovery concepts (SAR development, hit-to-lead, lead optimization, ADMET, DMPK assays).

· Experience developing software tools, libraries, or user-facing scientific interfaces.