Ai/ml Engineer - Vaccine Research

Pfizer Pfizer · Pharma · New York, NY

AI/ML Engineer role focused on vaccine research, applying AI/ML to discover and develop vaccines. Responsibilities include shaping scientific strategy with AI, owning foundational and predictive modeling end-to-end, advancing generative AI for vaccine design, engineering robust ML systems, decoding high-dimensional biology, and elevating AI fluency. Requires PhD or Master's with experience in AI/ML within life sciences, knowledge of vaccine R&D, and expertise in foundation models, generative AI, and ML system design.

What you'd actually do

  1. Design, develop, and deploy AI systems directly influence vaccine discovery and development decisions, informing antigen selection, experimental prioritization, translational strategies, and clinical study design.
  2. Lead AI initiatives spanning antigen discovery and optimization, experimental design, translational modeling, clinical trial simulation, patient stratification, and operational forecasting
  3. Apply state-of-the-art generative and foundation models to protein and antigen engineering.
  4. Architect reliable ML pipelines using modern MLOps practices across cloud and HPC environments, with strong attention to reproducibility, governance, and scientific credibility
  5. Integrate multimodal datasets – including omics, immunological data, clinical and real-world evidence, and scientific literature - to uncover biological insight and guide experimental and clinical decision-making.

Skills

Required

  • PhD in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline OR Master’s degree in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline and a minimum of 2 years of applied AI/ML experience in a Vaccines R&D, Life Sciences or other related discovery focused environment
  • Working knowledge of vaccine R&D workflows, including target identification, antigen design and optimization, translational science, clinical development, or portfolio analytics.
  • Demonstrated expertise in foundation model, predictive modeling, generative AI, and ML system design.
  • Strong programming skills in Python and modern ML frameworks (e.g., PyTorch, TensorFlow), with experience scaling models in cloud and/or HPC environments.

Nice to have

  • Experience operating fluently across disciplines - molecular biology, systems immunology, pharmacology, and statistics
  • Experience collaborating with Experimental Scientists, Clinicians, and cross-functional partners.
  • Clear scientific communicator with intellectual curiosity and a mission-driven mindset focused on improving patient outcomes

What the JD emphasized

  • directly inform scientific hypotheses, experimental design, and portfolio decisions
  • directly influence vaccine discovery and development decisions
  • Take models from concept through validation, deployment, and measurable scientific impact
  • Apply state-of-the-art generative and foundation models
  • rapidly prototype, rigorously evaluate, and responsibly deploy AI methods in high-stakes scientific contexts
  • Integrate multimodal datasets
  • translating cutting-edge advances into practical, defensible applications

Other signals

  • AI systems directly inform scientific hypotheses, experimental design, and portfolio decisions
  • Take models from concept through validation, deployment, and measurable scientific impact
  • Apply state-of-the-art generative and foundation models to protein and antigen engineering
  • Integrate multimodal datasets – including omics, immunological data, clinical and real-world evidence, and scientific literature