Associate Scientist, Postdoctoral Fellow – AI/ML and Agentic AI for Drug Discovery
About The Position
Our Research Laboratories' Postdoctoral Research Fellow Program aims to be a best-in-industry program for industrial postdoctoral researchers, designed to provide you with an academic focus in a commercial environment. With the resources, reach, and expertise of a large pharmaceutical company, postdoctoral researchers will be positioned to excel in an institution committed to breakthrough innovation in research and discovery. We are seeking a highly motivated scientist to fill a postdoctoral fellowship position in Modeling & Informatics (M&I), within Discovery Chemistry, at our company's Research Laboratories in South San Francisco, California. This is an exciting opportunity to conduct cutting-edge research in agentic artificial intelligence in an industry setting while learning about drug discovery at a high-caliber pharmaceutical company. The successful candidate will have access to high-performance computing resources and extensive internal data assets, collaborate with scientists from different research backgrounds, and apply modern AI/ML methods to automate and accelerate predictive modeling workflows in drug discovery. This role offers the chance to contribute to significant scientific publications and presentations at conferences, while gaining valuable experience in contemporary drug discovery.
Requirements
- Must currently hold a PhD OR Receive a Ph.D. no later than spring 2027
- Ph.D. in Machine Learning, Computer Science, Computational Chemistry, Cheminformatics, Chemical or Biomedical Engineering, Applied Mathematics, or a related discipline.
- Demonstrated expertise in machine learning, cheminformatics, or computational chemistry.
- Strong record of scientific achievement, as evidenced by peer-reviewed publications and presentations.
- Hands-on experience building, training, and rigorously evaluating machine learning models in Python.
- Experience designing, conducting, and interpreting computational experiments, including model benchmarking.
- Strong quantitative and analytical skills, including statistical analysis and data interpretation.
- Ability to work independently while also thriving in a highly collaborative, multidisciplinary environment.
- Excellent oral and written communication skills.
Nice To Haves
- Experience with large language models and agentic AI (e.g., LangGraph, LangChain).
- Proficiency with modern ML frameworks and experiment-tracking tools (e.g., PyTorch, MLflow).
- Experience applying graph neural networks, chemical foundation models, or other modern deep learning approaches to molecular or drug discovery data.
- Experience with predictive modeling in drug discovery.
- Experience with cheminformatics tools (e.g., RDKit).
- Experience developing or improving agentic AI systems through feedback, adaptation, or alignment techniques.
- Demonstrated creativity and scientific independence in developing and executing research projects.
Responsibilities
- Design, execute, and interpret research that benchmarks standard AI/ML methods across internal and external drug discovery datasets.
- Develop and drive an independent research project to design and evaluate AI agent frameworks for drug discovery modeling.
- Refine agentic AI workflows to improve transparency, robustness, and decision-making.
- Analyze complex drug discovery datasets using appropriate statistical and machine learning tools.
- Present research findings at internal scientific forums, external conferences, and in peer-reviewed scientific journals.
Benefits
- medical, dental, vision healthcare and other insurance benefits (for employee and family)
- retirement benefits, including 401(k)
- paid holidays
- vacation
- compassionate and sick days
- annual bonus
- long-term incentive
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
