Director of AI Engineering R&D

Position: Director of AI Engineering Pfizer R&D
** Where frontier AI meets world-class science to accelerate medicines to patients
** Pfizer is building an AI-first R&D engine—one where AI is not a support function, but a core scientific capability shaping how medicines are discovered, developed, and delivered.

We are recruiting AI Engineers to be embedded into the various scientific disciplines of R&D including, Target Discovery, Medicinal and Biomedicine Design, ADME (Absorption, Distribution, Metabolism, Excretion), Translational & Genomics Medicine, Clinical Manufacturing, Preclinical Toxicology, Clinical Trial Design & Execution, Medical Functions, Real World Experience, Global Regulatory functions, Safety and Pharmacovigilance. You will help drive the discovery and development of Pfizer’s next generation of breakthrough medicines.

As a Director of AI Engineering, embedded within one of our core scientific disciplines, you’ll work shoulder-to-shoulder with leading scientists and clinicians to translate complex biology into new therapies, supported by AI models.
Your models won’t live in notebooks—they’ll influence molecules selected, studies designed, and patients treated.

If you’re a rising AI technical leader (2–5 years post-graduate training) from a top research environment who thrives at the intersection of AI, biology, and real-world impact, this is an opportunity to help define how AI is applied and practiced in modern medicine, potentially impacting the lives’ of patients globally.
** What you’ll do** (you could be involved in one or more of these tasks, pending your expertise and interests):
* ** 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 choices on 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—take 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: cloud and HPC environments, distributed training, reproducibility, governance, and observability—designed for scientific credibility and operational scale. Automate and standardize the entire lifecycle of ML systems, from initial development to long-term production maintenance, providing compliance and an audit trails.
* ** Decode high-dimensional biology
** Integrate multimodal data—omics, imaging, real-world evidence, and scientific literature—into representations that surface biological insight and guide experimental and clinical strategy.
* ** Influence portfolio and strategy decisions
** Partner with scientific and strategy leaders to model uncertainty, run scenario analyses, and optimize resource allocation across a complex R&D portfolio.
* ** Stay at the frontier
** Continuously assess emerging AI methods and tools, translating advances into practical, defensible applications for a specific R&D discipline
* ** Raise AI fluency across the organization
** Mentor scientists and engineers, foster hands-on curiosity, and help build a culture where rigorous experimentation and learning are the norm.
* ** Represent the science externally
** Publish, present, and engage with the broader AI and life-sciences community at leading conferences and forums.
** What you’ll bring
*** 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).
* A working understanding of R&D workflows is preferred but not required, across target identification, lead optimization, translational science, clinical design, operations…