Director of AI Engineering Pfizer R&D

Role Summary

Director of AI Engineering, embedded within one of Pfizer R&D’s core scientific disciplines, collaborating with scientists and clinicians to translate complex biology into new therapies supported by AI models. Models will influence molecules selected, studies designed, and patients treated.

Responsibilities

• Build AI that directly shapes R&D decisions—design, develop, and scale production-grade AI systems embedded in drug discovery and development programs.
• Own foundational and predictive modeling end-to-end—from molecular optimization and experimental design to clinical trial simulation, patient stratification, and operational forecasting.
• Advance generative AI for drug design—apply state-of-the-art generative approaches to molecular and protein engineering, prototype quickly, evaluate rigorously, and deploy responsibly.
• Engineer elegant, reliable ML systems—architect robust pipelines with modern MLOps, cloud and HPC environments, distributed training, reproducibility, governance, and observability; automate and standardize the ML lifecycle with compliance and audit trails.
• Decode high-dimensional biology—integrate multimodal data into representations that surface biological insight to guide experimental and clinical strategy.
• Influence portfolio and strategy decisions—model uncertainty, run scenario analyses, and optimize resource allocation across a complex R&D portfolio.
• Stay at the frontier—assess emerging AI methods and translate advances into practical applications for a specific R&D discipline.
• Raise AI fluency across the organization—mentor scientists and engineers, fostering a culture of rigorous experimentation and learning.
• Represent the science externally—publish, present, and engage with the AI and life-sciences community at conferences and forums.

Qualifications

• PhD or Master’s in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline.
• AI native; 2–5 years of applied AI/ML experience.
• Experience in life sciences preferred, but not required (pharma, biotech, or health tech).
• Working understanding of R&D workflows across target identification, lead optimization, translational science, clinical design, operations forecasting, or portfolio analytics (preferred but not required).
• Comfort operating across disciplines—chemistry, biology, pharmacology, statistics—with ability to ground models in biological and clinical reality.
• Demonstrated expertise in predictive modeling, generative AI, and ML system design.
• Strong programming skills in Python and modern ML frameworks (e.g., PyTorch, TensorFlow); experience scaling models in cloud and/or HPC environments.
• Proven ability to collaborate with scientists, clinicians, product teams, and business leaders.
• Clear scientific communication, intellectual curiosity, and a mission-driven mindset focused on improving patient outcomes.

Skills

• Predictive modeling
• Generative AI
• ML system design
• Python programming
• PyTorch, TensorFlow
• ML in cloud/HPC environments
• Multimodal data integration
• Scientific collaboration and communication

Education

• PhD or Master’s in relevant field as listed in Qualifications