Scientist, Structural Chemistry
United States - California - Foster CityResearchRegularDescripción del trabajo
At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference.
Every member of Gilead’s team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact.
We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together.
Job Description
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.
For additional benefits information, visit:
https://www.gilead.com/careers/compensation-benefits-and-wellbeing
* Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans.
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