Director of AI Engineering Pfizer R&D

Role Summary

Director of AI Engineering embedded within Pfizer’s core scientific disciplines, collaborating with leading scientists to translate complex biology into new therapies supported by AI models. Models will influence molecules, studies, and patient treatment decisions rather than existing only in notebooks. Role involves shaping AI-driven medicine across R&D in areas such as Target Discovery, Medicinal Design, Translational & Genomics Medicine, and Clinical Development Operations, with on-site collaboration at major innovation hubs.

Responsibilities

• Build AI that directly shapes R&D decisions: design, develop, and scale production-grade AI systems embedded in drug discovery and development programs where model outputs inform molecules, experiments, trials, and patient access to clinical trials.
• Own foundational and predictive modeling end-to-end: from molecular optimization and experimental design to clinical trial simulation, patient stratification, and operational forecasting—taking ideas from concept through validation, deployment, and measurable value.
• 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 in high-stakes scientific contexts.
• Engineer elegant, reliable ML systems: architect robust pipelines with modern MLOps in cloud and HPC environments, distributed training, reproducibility, governance, and observability; automate and standardize the entire lifecycle of ML systems with compliance and an audit trail.
• Decode high-dimensional biology: integrate multimodal data—omics, imaging, real-world evidence, and literature—into representations that surface biological insight and guide strategy.
• Influence portfolio and strategy decisions: model uncertainty, run scenario analyses, and optimize resource allocation across a complex R&D portfolio in collaboration with scientific and strategy leaders.
• 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, foster curiosity, and build a culture of rigorous experimentation and learning.
• Represent the science externally: publish, present, and engage with the broader AI and life-sciences community at leading conferences and forums.

Qualifications

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

Skills

• Predictive modeling
• Generative AI for molecular design
• ML system design and MLOps
• Multimodal data integration (omics, imaging, real-world evidence, literature)
• Cloud and HPC environments; distributed training; governance and observability
• Scientific collaboration and cross-disciplinary teamwork
• Strong communication and technical leadership

Education

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

Additional Requirements

• On-site/hybrid requirements: hybrid role with on-site work approximately 2.5 days per week at major Pfizer innovation hubs (e.g., Kendall Square, Cambridge, MA; Groton, CT; La Jolla, CA; Bothell/Seattle, WA)
• Relocation assistance may be available based on business needs