Director of AI Engineering Pfizer R&D

Role Summary

Director of AI Engineering embedded within core scientific disciplines to translate complex biology into new therapies, supported by AI models. Models will influence molecules selected, studies designed, and patients treated.

Location:

hybrid role with on-site presence at Kendall Sq, Cambridge, MA;
Groton, CT;
La Jolla, CA;
Bothell/Seattle, WA.

• Build AI that directly shapes R&D decisions by designing, developing, and scaling 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, prototyping quickly, evaluating rigorously, and deploying responsibly in high-stakes contexts.
• Engineer elegant, reliable ML systems with robust pipelines, MLOps, cloud/HPC environments, distributed training, governance, observability, and lifecycle automation.
• Decode high-dimensional biology by integrating multimodal data to surface biological insight and guide strategy.
• Influence portfolio and strategy decisions by modeling uncertainty, running scenario analyses, and optimizing resource allocation across a complex R&D portfolio.
• Stay at the frontier by assessing emerging AI methods and translating advances into practical applications for a specific R&D discipline.
• Raise AI fluency across the organization by mentoring scientists and engineers and fostering a culture of rigorous experimentation and learning.
• Represent the science externally by publishing, presenting, and engaging with the AI and life-sciences community.

• PhD or Master’s in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline.
• AI native with 2–5 years of applied AI/ML experience. Experience in life sciences preferred but not required.
• Working understanding of R&D workflows across target identification, lead optimization, translational science, clinical design, operations forecasting, or portfolio analytics is preferred but not required.
• Ability to operate across disciplines—chemistry, biology, pharmacology, statistics—and 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, Tensor Flow); 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.

• Predictive modeling
• Generative AI
• ML system design
• Python programming
• PyTorch, Tensor Flow
• Cloud and HPC scalability
• Multimodal data integration
• Cross-disciplinary collaboration

• PhD or Master’s in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline