Scientist, Structural Chemistry

At Gilead were creating a healthier world for all people. For more than 35 years weve 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 worlds biggest health challenges and our mission requires collaboration determination and a relentless drive to make a difference.

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Job Description

Gilead has defined Adopt and scale AI to transform how we work as one of the companys strategic 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 modelsincluding geometric deep learning generative models and co-folding architecturesfor 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 largescale 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:

Eligible employees may participate in benefit plans subject to the terms and conditions of the applicable plans.

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