Associate Scientist, Postdoctoral Fellow - Pharmacokinetics

Associate Scientist, Postdoctoral Fellow - Pharmacokinetics

Join the Postdoctoral Research Fellow Program  a part of a best‑in‑industry program that provides an academic focus in a commercial environment. Our Research Laboratories’ Postdoctoral Research Fellow Program is designed to help industrial postdoctoral researchers excel in an institution committed to breakthrough innovation in research and discovery.

The Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) function is seeking a highly motivated postdoctoral fellow with expertise in machine learning to help transform drug discovery and preclinical development. You will join an interdisciplinary team, collaborating closely with research partners across our global organization. Your work will invent, prototype, and apply advanced machine‑learning methods — particularly in generative modeling and related areas — to expand our capabilities in designing, prioritizing, and characterizing novel therapeutic candidates.

• Conduct original research to develop state‑of‑the‑art AI / machine‑learning methods for drug discovery (e.g., molecular generative models, multi‑objective optimization, property prediction, active learning, document authoring, document generation, hybrid AI system, multi‑agent system).
• Design and execute experiments, analyze results rigorously, and iterate rapidly on model architectures and training strategies.
• Build robust, reproducible code and workflows; contribute to shared libraries and documentation.
• Collaborate with chemists, biologists, PDMB scientists, and data / ML engineers to translate methods into impactful applications.
• Communicate findings through internal presentations and peer‑reviewed publications; present at conferences and workshops.

• Ph.D. (or completion within six months) in Computer Science, Statistics, Physics, Applied Mathematics, Bioinformatics, Computational Biology, Chem/Informatics, Engineering, or a related field.

• Demonstrated research excellence and problem‑solving ability; strong motivation to learn, innovate, and deliver.
• Proficiency in core ML / statistics topics such as probability, statistical inference, optimization, discrete mathematics / algorithms, and/or probabilistic modeling.
• Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, Tensor Flow).
• Track record of publications and/or presentations in ML, computational chemistry / biology, or related fields.
• Excellent collaboration and communication skills; proven ability to work in cross‑functional teams.

• Experience with molecular representations (e.g., SMILES, graphs), generative models (e.g., diffusion models, VAEs, flow models), and sequence / structure models (e.g., transformers, GNNs, protein or RNA models).
• Familiarity with cheminformatics / biophysics toolkits (e.g., RDKit), docking or molecular simulation, ADMET modeling, or DMPK‑relevant endpoints.
• Practical experience with experimental design, active learning, uncertainty quantification, or multi‑objective optimization.
• Software engineering best practices (Git, testing, containers) and experience working with large datasets and cloud / GPU environments.

$70,500.00 – $. The range reflects expected pay for this role at the time of posting. Actual compensation depends on education, experience, geography, and other factors.

Comprehensive package including medical, dental, vision, health‑care and other insurance benefits for employee and family; retirement benefits (401(k)); paid holidays, vacation, and compassionate & sick days. More information at

US and Puerto Rico Residents only. Apply through  Applicants with arrest and conviction records will be considered in compliance with the San Francisco Fair Chance Ordinance; applicants from Los Angeles will be considered in accordance with the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance.

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