Postdoctoral Fellow, AI/ML Applications for Vaccine Full-time NEW

Use Your Power for Purpose

At Pfizer, our purpose is to deliver breakthroughs that transform patients' lives. Central to this mission is our Research and Development team, which strives to convert advanced science and cutting-edge technologies into impactful therapies and vaccines. Whether you are engaged in discovery sciences, ensuring drug safety and efficacy, or supporting clinical trials, your role is crucial. You will leverage innovative design and process development capabilities to expedite the delivery of top-tier medicines to patients globally.

What You Will Achieve

In this role, working at the interface of viral genomics, antigenicity modeling, evolutionary forecasting, and deep learning, you will design, implement, and validate AI-driven models for prospective vaccine strain selection. More specifically, you will:

• Develop sequence-based deep learning models for rapidly evolving virus, including:
• Transformer or language-model-based architectures for viral protein sequences
• Graph neural networks that predict time-dependent changes in strain dominance
• Integrate multi-source surveillance, immunogenicity, and vaccine efficacy data to compute and evaluate prospective coverage scores for candidate vaccine strains
• Utilize interpretation frameworks to identify key features for virus evolutionary advantage related to infectious disease burden and vaccine antigen design
• Conduct rigorous retrospective and prospective benchmarking validation. Iterative fine-tuning to improve model performance
• Communicate complex data and results clearly to both technical and non-technical stakeholders. Collaborate extensively with those from other scientific disciplines within the group, from other subdivisions of Pfizer, and potentially from external partners
• Publish impactful scientific findings while safeguarding confidential data, ensuring clear, transparent reporting of methods and results to facilitate reproducibility and recognition in peer-reviewed journals and conferences

Minimum Requirements

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Machine Learning, or a closely related field
• Demonstrated ability to independently design and implement complex ML models, evidenced by first-author publications or equivalent open-source research contributions
• Strong hands-on experience with deep learning for sequence data, including:
• Transformer or language-model architectures
• Model training, validation, and benchmarking on large biological datasets
• Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), with experience managing full modeling pipelines and statistical modeling, including regression analysis and mixed-effects models
• Experience working with viral or microbial sequence data, including alignment, curation, and longitudinal analysis across time
• Less than 2 years of post-degree experience
• Two letters of recommendation must be provided prior to interview
• Willingness to make a minimum 2-year commitment
• Strong communication and collaboration skills with the ability to work effectively in a hybrid team environment. Strong organizational skills and attention to detail in managing deadlines and documentation
• Ability to clearly communicate complex modeling concepts and results to both technical and biological audiences

Preferred Qualifications

• Direct experience with viral evolution modeling, fitness/dominance prediction, or time-resolved sequence forecasting
• Experience building or extending protein language models or MSA-based neural networks for biological inference
• Familiarity with antigenicity data or related experimental measurements, and how such data can be integrated into machine learning models
• Knowledge of SHAP or similar model interpretation frameworks for feature attribution in complex models
• Prior work on influenza, SARS-CoV-2, or other rapidly evolving viruses, particularly in the context of immune escape, antigenic drift, or vaccine design

Additional Information

• Relocation support available
• Location: On premise