Postdoctoral Fellow – R&D Science Engineer
Pfizer
full-time
Posted on:
Location Type: Office
Location: California • Connecticut • United States
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Tech Stack
About the role
- 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 metanalyses of largescale, 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.
Requirements
- 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.
- Early career researcher (no more than 2-years of postdoc working experience)
- Able to commit to a minimum two-year postdoctoral fellowship.
- Demonstrated scientific achievement through first author publications, peer reviewed contributions, or significant scientific presentations.
- Experience applying AI/ML to real world problems, including predictive modeling, generative models, representation learning, or ML system design.
- Proficiency in Python and modern ML frameworks such as PyTorch and/or TensorFlow.
- Experience working with large, complex, or heterogeneous datasets, including data curation, model evaluation, and scalable computing environments (cloud and/or HPC).
- Ability to collaborate across disciplines including biology, chemistry, pharmacology, statistics, engineering, or clinical science, translating computational approaches into scientific context.
- Familiarity with reproducible and responsible AI practices, including version control, transparent reporting, and awareness of model limitations and bias.
- Strong communication skills, intellectual curiosity, and a mission driven interest in advancing science and improving patient outcomes.
Benefits
- Relocation assistance may be available based on business needs and/or eligibility.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
AI methodsmachine learningtransfer learningrepresentation learningdomain adaptationpredictive modelinggenerative modelsmodel evaluationdata curationscalable computing
Soft Skills
strong communication skillsintellectual curiositymission driven interest
Certifications
PhD in relevant discipline