AI​/ML Engineer - Vaccines Modeling and Antigen Discovery

We are looking to identify an AI/ML Engineer – Vaccines Modeling and Antigen Discovery who will play a strategic role at the intersection of artificial intelligence, vaccinology, and translational science. You will help define the future of AI‑driven vaccinology, with the potential to impact human lives globally. Embedded within Vaccines R&D, supporting viral and bacterial vaccine programs, you will help define how advanced AI methods are applied to unlock new biological insights and accelerate next‑generation vaccine innovation.

As an AI Science Engineer, you will work shoulder‑to‑shoulder with leading Scientists, Immunologists, and Clinicians to translate complex biology into differentiated vaccine strategies. Your work will go beyond proof‑of‑concept models—your AI systems will directly inform scientific hypotheses, experimental design, and portfolio decisions across the vaccine pipeline.

• Model immune response and translational biology
• Conduct the development of predictive and multimodal models of vaccine‑induced immune responses, including humoral, cellular, systems immunology, and durability readouts.
• Advance translational modeling approaches that connect preclinical, clinical, epidemiological, and real‑world evidence to support dose selection, patient stratification, correlates of protection, and clinical study design.
• Accelerate novel antigen discovery and rational vaccine design
• Apply foundation models, generative AI, structure‑aware learning, protein language models, and sequence‑to‑function modeling to identify, optimize, and prioritize novel vaccine antigens.
• Integrate computational antigen design with experimental feedback loops to improve immunogenicity, breadth, manufacturability, develop ability, and translational relevance.
• Build AI‑ready data foundations and advance agentic AI and organizational AI fluency.
• Partner across scientific, clinical, data, and technology teams to make vaccine R&D data AI‑ready through rigorous data standards, metadata, ontologies, quality control, provenance, governance, and reusable data products.
• Design and responsibly deploy agentic AI workflows that support scientific reasoning, literature synthesis, knowledge management, experimental planning, model interpretation, and decision traceability in Vaccine R&D.
• Mentor Scientists, Engineers, and cross‑functional partners to raise AI fluency, promote responsible adoption, and represent Pfizer’s scientific leadership at the interface of AI, immunology, infectious disease, and vaccine innovation.

• 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, with emphasis on immune‑response modeling, antigen discovery and optimization, and translational science.
• Demonstrated ability to develop and apply quantitative and AI/ML‑enabled approaches for translating preclinical immunogenicity, efficacy, and systems‑biology readouts into clinically relevant predictions.
• Proven experience working across cross‑functional and multidisciplinary teams, including immunology, molecular biology, serology, bioinformatics and data science, to ensure AI models are biologically grounded, experimentally actionable, and clinically meaningful.
• 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, Tensor Flow), with experience scaling models in cloud and/or HPC environments.
• 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.

Work Location Assignment:
This is a hybrid role requiring you to live within commuting distance and work on‑site…