Scientist, Structural Chemistry AI/ML

职位描述

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 Scientist 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.

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 0+ years relevant research experience to the position such as postdoctoral roles, 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.