About the job
Drives practical application of large language models (LLMs) and agentic artificial intelligence across Inflammation & Immunology (I&I) by identifying high-value scientific use cases, building reusable workflows, and partnering closely with scientists to ensure adoption, rigor, and impact. This role sits within AI Delivery & Enablement (AIDE), the Systems Immunology line created to make AI and omics workflows practical across I&I by converting repeat asks into reusable capabilities and helping those capabilities become embedded in day-to-day scientific work.
Responsibilities
Identify high-value, repeat scientific and analytical use cases where LLMs, agentic AI, and workflow automation can materially improve the speed, quality, consistency, or accessibility of work across I&I.
Design, build, and refine reusable AI workflows, prompt/program structures, orchestration patterns, and agent-based tools that support end-to-end scientific narratives rather than isolated task completion.
Partner closely with scientists, clinicians, computational biologists, and other stakeholders to understand real workflow pain points, define fit-for-purpose solutions, and iterate rapidly toward tools that are scientifically useful and operationally adopted.
Translate emerging LLM and agent capabilities into practical scientific applications, balancing speed of experimentation with methodological rigor, grounded usage, human-in-the-loop design, and reusable implementation patterns.
Contribute to technical standards for evaluation, documentation, guardrails, and workflow quality so that AIDE solutions are trusted, reproducible, and suitable for repeated use across teams and projects.
Work across the Digital ecosystem to avoid duplication, leverage existing platforms where appropriate, and ensure that AIDE solutions fit within the broader Pfizer AI and data environment.
Raise AI fluency among collaborators by demonstrating practical workflows, explaining trade-offs clearly, and helping scientists build confidence in responsible use of LLM-enabled tools.
Qualifications
Minimum
Bachelors degree and 6+ years of relevant work experience OR Master’s degree and 5+ years of experience OR PhD and 1+ years of experience. Advanced degree in computer science, machine learning, artificial intelligence, computational biology, bioinformatics, statistics, engineering, or a related quantitative or scientific field preferred.
Strong hands-on experience applying LLMs, generative AI, machine learning, or related AI approaches to real-world workflows, products, or analytical use cases.
Demonstrated ability to build practical, reusable workflows or systems rather than one-off analyses, with strong implementation skills in Python and modern AI / ML tooling.
Experience working directly with domain users or stakeholders to translate ambiguous needs into useful technical solutions, with evidence of strong collaboration and communication skills.
Sound judgment regarding methodological rigor, model limitations, evaluation, and the appropriate role of human oversight in AI-enabled workflows.
Preferred
Experience in life sciences, pharma, biotech, systems biology, immunology, translational science, omics, or related research environments.
Familiarity with scientific evidence synthesis, literature and document workflows, retrieval-augmented approaches, or multi-step knowledge workflows involving unstructured and structured scientific information.
Experience with agentic orchestration, prompt/program design, workflow automation, or multimodal extensions of AI systems.
Comfort operating across scientific and technical disciplines, with enough domain fluency to engage credibly with scientists while still bringing a strong applied-AI builder mindset.