Postdoc Fellow - Modeling & AI for Drug Discovery

Merck & Co.; South San Francisco (CA); Hybrid; USD 82,000 - 92,000; Full time

Benefits offered by this job

Annual bonus

Long-term incentive

Medical, dental, vision insurance

401(k) retirement benefits

Job summary

Merck & Co. invites applications for a Postdoctoral Research Fellow in Modeling & Informatics at our South San FranciscoDiscovery Chemistry group.

The role focuses on agentic AI to accelerate drug discovery, with access to HPC and internal datasets, collaborating across scientific disciplines and publishing work in peer-reviewed venues. Ideal candidates have strong ML and cheminformatics expertise, hands-on Python experience, and a track record of independent, innovative research in academia or

Qualifications

• PhD in a relevant field or PhD by spring 2027.
• Experience designing, conducting and interpreting computational experiments with ML benchmarking.

Responsibilities

• Design, execute, and interpret research benchmarking AI/ML methods on drug discovery data.
• 1) Develop and drive independent research on AI agent frameworks for drug discovery modeling.
• 2) Refine agentic AI workflows for transparency, robustness, and decision-making.
• 3) Analyze complex datasets using statistical and ML tools.
• 4) Present findings at internal forums, conferences and journals.

Skills

AI Agents

Artificial Intelligence

Chemical Informatics

Computational Biology

Computational Chemistry

Computational Models

Machine Learning

Python

Statistical Analysis

Education

PhD in Machine Learning, Computer Science, Computational Chemistry, Cheminformatics, Chemical/Biomedical Engineering, Applied Mathematics

Tools

LangGraph

LangChain

PyTorch

MLflow

RDKit

Job description

Merck & Co. invites applications for a Postdoctoral Research Fellow in Modeling & Informatics at our South San FranciscoDiscovery Chemistry group.

The role focuses on agentic AI to accelerate drug discovery, with access to HPC and internal datasets, collaborating across scientific disciplines and publishing work in peer-reviewed venues. Ideal candidates have strong ML and cheminformatics expertise, hands-on Python experience, and a track record of independent, innovative research in academia or