Senior AI​/ML Engineer - Research Data AI and Predictive Modeling; Vaccine R&D

POSITION SUMMARY

Vaccines Research is seeking a highly innovative and technically accomplished AI/ML Engineering leader to accelerate the transformation of scientific data into a strategic asset for AI-driven vaccine discovery and development.

This role sits at the intersection of artificial intelligence, data engineering, and vaccine science. Embedded within Vaccines Research and supporting viral/bacterial vaccine programs, the successful candidate will lead implementation of a modern AI-ready research data ecosystem that enables advanced analytics, predictive modeling, generative AI applications, and agentic scientific workflows.

Vaccine research generates exceptionally diverse data, including antigen and pathogen sequences, immunological assays, omics datasets, imaging data, laboratory workflows, electronic lab notebooks, and study metadata. The scientific value lies not within individual datasets, but in the ability to connect, contextualize, and operationalize information across these modalities.

You will partner closely with Scientists, Bioinformaticians, Digital teams, and enterprise stakeholders to establish data foundations that make research data discoverable, interoperable, reusable, and AI-ready. These capabilities will power next-generation predictive and translational models that inform vaccine design, candidate prioritization, and decision-making across the research portfolio.

• Drive implementation of Vaccines Research's strategy for transforming diverse research data assets into scalable, AI-ready resources.
• Design and establish integrated data architectures that connect heterogeneous scientific datasets across laboratory, preclinical, and clinical domains.
• Develop automated data ingestion, transformation, and orchestration pipelines that convert fragmented research data into standardized, machine-readable assets.
• Define and implement semantic data frameworks, metadata standards, ontologies, and knowledge representations that improve interoperability, discoverability, and reuse.
• Build and advance knowledge graphs, retrieval systems, and graph-RAG capabilities that enable scientists and AI systems to interact effectively with both structured and unstructured research knowledge.
• Partner with enterprise data and digital organizations to ensure alignment with broader R&D data standards, platforms, and AI initiatives.

• Develop and deploy machine learning approaches that leverage linked multimodal datasets to generate insights into vaccine-induced immune responses and mechanisms of protection.
• Apply AI and predictive modeling techniques to support vaccine candidate evaluation, immunogenicity assessment, translational research, and portfolio decision-making.
• Advance approaches that integrate preclinical, clinical, epidemiological, and real-world datasets to improve scientific understanding and accelerate vaccine development.

• Translate strategic AI objectives into scalable technical roadmaps, architectures, and implementation plans.
• Serve as a technical leader and trusted partner across immunology, microbiology, bioinformatics, clinical research, digital, and data science organizations.
• Identify opportunities to modernize research workflows through AI-enabled automation and intelligent data integration.
• Act as a key liaison between Vaccines Research and broader Pfizer R&D AI, data, and digital communities, ensuring vaccine-specific needs are represented while leveraging enterprise capabilities whenever possible.

• Evaluate and implement emerging AI technologies, including foundation models, agentic AI systems, multimodal learning approaches, and generative AI capabilities relevant to vaccine research.
• Mentor scientists and technical teams in AI best practices, responsible AI adoption, and data-centric approaches to scientific discovery.
• Represent Vaccines Research in cross-functional AI initiatives and contribute to shaping the future of AI-enabled R&D.

• 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 4 years of applied AI/ML experience in R&D, Life Sciences or other related discovery…