Senior AI/ML Engineer - Research Data AI and Predictive Modeling (Vaccine R&D)
Pfizer
August 14, 2026
Remote friendly (Pearl River, NY)
United States
IT
WHAT YOU’LL DO
Lead Research Data AI-Readiness strategy and Implementation
- Drive implementation of strategy to transform diverse research data into scalable, AI-ready resources.
- Design integrated data architectures connecting heterogeneous scientific datasets across lab, preclinical, and clinical domains.
- Build automated data ingestion, transformation, and orchestration pipelines.
- Define semantic data frameworks, metadata standards, ontologies, and knowledge representations for interoperability and reuse.
- Build knowledge graphs, retrieval systems, and graph-RAG capabilities for structured and unstructured research knowledge.
- Partner with enterprise data/digital organizations to align with broader R&D data standards, platforms, and AI initiatives.
Advance Predictive and Translational Modeling
- Develop and deploy ML using linked multimodal datasets to understand immune responses and mechanisms of protection.
- Apply AI/predictive modeling for vaccine candidate evaluation, immunogenicity, translational research, and portfolio decision-making.
- Integrate preclinical, clinical, epidemiological, and real-world datasets.
Technical Leadership and Cross-functional Influence
- Translate AI objectives into technical roadmaps, architectures, and implementation plans.
- Lead across immunology, microbiology, bioinformatics, clinical research, digital, and data science teams.
- Modernize workflows via AI-enabled automation and intelligent data integration.
- Serve as liaison between Vaccines Research and broader R&D AI/data/digital communities.
Advance AI Adoption and Scientific Innovation
- Evaluate and implement foundation models, agentic AI, multimodal learning, and generative AI for vaccine research.
- Mentor teams on AI best practices, responsible AI, and data-centric discovery.
Minimum Qualifications
- PhD (or MS + 4 years applied AI/ML in R&D/life sciences/discovery).
- Proven experience architecting/implementing data-intensive AI/ML on complex scientific/clinical/real-world datasets.
- Experience creating scalable, reusable AI-ready data products/ecosystems.
- Strong Python and frameworks (PyTorch/TensorFlow or equivalent).
- Experience with data architectures, integration frameworks, semantic data models, metadata standards, knowledge graphs.
- Cloud and/or HPC experience.
- Strong collaboration/communication across scientific and technical teams.
- Ability to influence technical direction across cross-functional teams.
Preferred Qualifications
- Life sciences data standards/ontology frameworks (e.g., FAIR).
- Experience with immunology, systems biology, multi-omics, flow cytometry, imaging, vaccine, infectious disease datasets.
- Experience with generative AI, RAG, agentic AI, foundation models.
- Knowledge of translational modeling, biomarker development, clinical data science, or RWE.
- Publications, patents, open-source, or recognized AI/life sciences technical leadership.
Lead Research Data AI-Readiness strategy and Implementation
- Drive implementation of strategy to transform diverse research data into scalable, AI-ready resources.
- Design integrated data architectures connecting heterogeneous scientific datasets across lab, preclinical, and clinical domains.
- Build automated data ingestion, transformation, and orchestration pipelines.
- Define semantic data frameworks, metadata standards, ontologies, and knowledge representations for interoperability and reuse.
- Build knowledge graphs, retrieval systems, and graph-RAG capabilities for structured and unstructured research knowledge.
- Partner with enterprise data/digital organizations to align with broader R&D data standards, platforms, and AI initiatives.
Advance Predictive and Translational Modeling
- Develop and deploy ML using linked multimodal datasets to understand immune responses and mechanisms of protection.
- Apply AI/predictive modeling for vaccine candidate evaluation, immunogenicity, translational research, and portfolio decision-making.
- Integrate preclinical, clinical, epidemiological, and real-world datasets.
Technical Leadership and Cross-functional Influence
- Translate AI objectives into technical roadmaps, architectures, and implementation plans.
- Lead across immunology, microbiology, bioinformatics, clinical research, digital, and data science teams.
- Modernize workflows via AI-enabled automation and intelligent data integration.
- Serve as liaison between Vaccines Research and broader R&D AI/data/digital communities.
Advance AI Adoption and Scientific Innovation
- Evaluate and implement foundation models, agentic AI, multimodal learning, and generative AI for vaccine research.
- Mentor teams on AI best practices, responsible AI, and data-centric discovery.
Minimum Qualifications
- PhD (or MS + 4 years applied AI/ML in R&D/life sciences/discovery).
- Proven experience architecting/implementing data-intensive AI/ML on complex scientific/clinical/real-world datasets.
- Experience creating scalable, reusable AI-ready data products/ecosystems.
- Strong Python and frameworks (PyTorch/TensorFlow or equivalent).
- Experience with data architectures, integration frameworks, semantic data models, metadata standards, knowledge graphs.
- Cloud and/or HPC experience.
- Strong collaboration/communication across scientific and technical teams.
- Ability to influence technical direction across cross-functional teams.
Preferred Qualifications
- Life sciences data standards/ontology frameworks (e.g., FAIR).
- Experience with immunology, systems biology, multi-omics, flow cytometry, imaging, vaccine, infectious disease datasets.
- Experience with generative AI, RAG, agentic AI, foundation models.
- Knowledge of translational modeling, biomarker development, clinical data science, or RWE.
- Publications, patents, open-source, or recognized AI/life sciences technical leadership.