Postdoctoral Fellow-R&D Science Engineer

Role Summary

Pfizer is building an AI-first R&D engine where artificial intelligence is a foundational scientific force, redefining how medicines are discovered, developed, and delivered from molecule to patient. As part of Pfizer’s R&D Postdoctoral Fellow Program, AI-focused science engineers are embedded where science happens, working directly within core research organizations and collaborating across the R&D enterprise to tackle challenges in Internal Medicine, Immunology, Infectious Diseases, Oncology, Vaccines, and other areas to help drive the discovery and development of breakthrough medicines. As an AI R&D Postdoctoral Fellow, you will work shoulder-to-shoulder with scientists, clinicians, and drug developers, translating complex biology and clinical challenges into deployable AI solutions that inform molecular design, study strategy, and decision-making across the drug development lifecycle. This program is built for emerging science engineers who want their work to matter beyond publications, applying AI at the intersection of biology and medicine to improve patient outcomes.

Responsibilities

• Design, develop, and apply advanced AI and machine learning methods to address high impact scientific challenges across the drug discovery and development lifecycle.
• Translate models across datasets, modalities, and disease areas using approaches such as transfer learning, representation learning, and domain adaptation.
• Perform integrative and meta-analyses of large-scale, multisource datasets to generate robust, generalizable insights.
• Curate, harmonize, and quality control complex scientific and clinical datasets to enable rigorous statistical analysis and machine learning model development.
• Collaborate closely with multidisciplinary teams spanning biology, chemistry, pharmacology, clinical science, and data science to ensure AI solutions are scientifically grounded and decision relevant.
• Communicate scientific findings clearly and transparently through internal forums and external publications, producing high-impact, peer-reviewed work while safeguarding confidential information and supporting reproducibility.

Qualifications

• Required: PhD in a relevant discipline such as Computer Science, Machine Learning, Artificial Intelligence, Biomedical Engineering, Applied Mathematics, Statistics or Biostatistics, Computational Biology, Systems Biology, Computational Chemistry, Bioinformatics, Biomedical Informatics, Immunology, or a related field.
• Required: Early career researcher (no more than 2-years of postdoc working experience).
• Required: Able to commit to a minimum two-year postdoctoral fellowship.
• Required: Demonstrated scientific achievement through first author publications, peer reviewed contributions, or significant scientific presentations.
• Required: Experience applying AI/ML to real world problems, including predictive modeling, generative models, representation learning, or ML system design.
• Required: Proficiency in Python and modern ML frameworks such as PyTorch and/or TensorFlow.
• Required: Experience working with large, complex, or heterogeneous datasets, including data curation, model evaluation, and scalable computing environments (cloud and/or HPC).
• Required: Ability to collaborate across disciplines including biology, chemistry, pharmacology, statistics, engineering, or clinical science, translating computational approaches into scientific context.
• Required: Familiarity with reproducible and responsible AI practices, including version control, transparent reporting, and awareness of model limitations and bias.
• Required: Strong communication skills, intellectual curiosity, and a mission driven interest in advancing science and improving patient outcomes.
• Preferred: Exposure to applied research or R&D workflows where analytical outputs inform decisions.
• Preferred: Code or reproducible research artifacts (e.g., GitHub, GitLab).