Post-Doc Scientist, Structural Biology and Chemistry – Gilead Sciences, United States

Summary

Gilead Sciences is seeking a highly motivated, attentive, and self-driven Postdoctoral Scientist to join the Computational Modeling Group within the Structural Biology and Chemistry department. As part of a 3-year fixed-term postdoctoral training program, the successful candidate will work at the cutting edge of AI/ML applied to small molecule drug discovery, evaluating and deploying state-of-the-art machine learning models across hit identification, hit-to-lead, and lead optimization stages.

Post-Doc Scientist, Structural Biology and Chemistry – Gilead Sciences

Quick Overview

Designation

• Post-Doc Scientist (Structural Biology and Chemistry / Computational Modeling)

Research Area

• Artificial Intelligence & Machine Learning (AI/ML) in Small Molecule Drug Discovery
• Computational Chemistry & Cheminformatics
• Structural Biology & Protein Co-folding
• Virtual Screening of Ultra-Large Chemical Libraries

Location

• Foster City, California, United States (Onsite Required)

Eligibility / Qualification

Minimum Qualifications:

• Ph.D. in Computational Chemistry, Cheminformatics, Computational Biology, Computer Science, Biophysics, Chemistry, or a related discipline with 0+ years of relevant post-degree experience.
• Deep expertise in AI/ML applications in drug discovery and/or Computational Chemistry.

Knowledge, Experience, and Skills:

• Solid understanding of machine learning architectures relevant to chemistry, molecular modeling, and structural biology.
• Strong programming proficiency in Python and standard ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
• Demonstrated ability to design, implement, and evaluate robust model validation strategies, including uncertainty quantification and applicability domain assessment.
• Experience with cheminformatics toolkits (e.g., RDKit, OpenEye, or Schrödinger suite).
• Strong critical thinking, problem-solving, planning, and interpersonal communication skills.
• Track record of research productivity with peer-reviewed publications.

Job Description & Key Responsibilities

• AI/ML Evaluation: Perform detailed evaluation and analysis of AI tools and ML models impacting various stages of small molecule drug discovery (hit identification, hit-to-lead, lead optimization).
• Predictive Modeling: Leverage Gilead’s rich historical datasets and structures to evaluate co-folding tools, potency models, and ADME property prediction models in partnership with computational chemists and AI/ML scientists.
• Virtual Screening: Implement and analyze advanced methods for ultra-large library virtual screening, including active learning and Thompson sampling workflows.
• Degrader Property Prediction: Investigate the feasibility of ML models to predict physicochemical and pharmacokinetic properties of heterobifunctional degraders (e.g., EPSA, permeability) starting from SMILES strings or 3D conformational ensembles.
• Dissemination: Present scientific findings internally and externally, and publish cutting-edge research in high-impact, peer-reviewed scientific journals.

How to Apply

1. Visit the official Gilead Careers Portal link:Gilead Careers Job Posting (R0053915-1)
2. Click the “Apply” button to start your online application.
3. Complete the candidate profile, upload your updated CV/Resume, cover letter, and contact information for professional references.

Last Date for Apply

• Open Until Filled / Rolling Basis (Early application is highly encouraged).

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