Senior Principal AI Research Engineer

As a Senior Principal AI Research Engineer at Pfizer, you will build machine learning systems that transform innovative models into secure, reliable, and scalable capabilities for scientists across Pfizer.

You will own critical AI capabilities from architecture through production deployment, leading the design and delivery of end-to-end systems that directly enable scientific discovery.

Your work will support researchers tackling some of the toughest challenges in medicine, helping accelerate insights across discovery, development, and translational science.

You will work across technologies and domains including:

• Large Language Models (LLMs)
• Agentic AI Systems
• Multimodal Learning
• Scientific Machine Learning
• Retrieval-Augmented Generation (RAG)
• Knowledge Graphs
• Distributed Training and Inference
• AI Platforms and MLOps
• AI Evaluation and Benchmarking

You will:

• Design, develop, and deploy production-grade AI systems supporting multimodal biomedical models and intelligent agents
• Engineer data pipelines, retrieval systems, evaluation frameworks, knowledge systems, inference services, and human-in-the-loop workflows
• Create scalable software that enables AI capabilities to move beyond experimentation and into everyday scientific use
• Translate scientific and business needs into technical solutions
• Lead architecture, implementation, testing, deployment, monitoring, and continuous improvement for your area of ownership
• Drive projects from concept through production and operational support
• Partner closely with AI scientists, computational biologists, data engineers, and platform teams
• Transform promising prototypes into reusable, reliable, and maintainable systems
• Develop solutions with clear interfaces, measurable performance, and long-term sustainability
• Balance performance, scalability, cost, reliability, security, privacy, maintainability, and speed of delivery
• Apply sound engineering judgment when evaluating technical approaches and system architecture decisions
• Strengthen model evaluation, observability, reproducibility, lineage, security, and operational resilience
• Implement practices that support responsible and trustworthy AI deployment within scientific environments
• Contribute to architecture reviews, code reviews, engineering standards, and reusable technical patterns
• Troubleshoot complex problems and improve engineering quality across the organization
• Provide guidance and support to less-experienced engineers through collaboration and mentorship
• Define success metrics and operational indicators
• Continuously improve performance, reliability, usability, and scientific impact
• Help teams adopt AI with confidence and scale

The most successful engineers in this role:

• Take ownership and consistently deliver
• Balance innovation with engineering discipline
• Embrace difficult technical challenges
• Learn quickly and continuously
• Build trust through execution and collaboration
• Help elevate the capabilities of the broader team
• Stay focused on outcomes and scientific impact

Pfizer offers comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life's moments.