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Pfizer · 22 hours ago

Director of AI Engineering Pfizer R&D

United States - California - La Jolla

Full-time

Hybrid

Lead/Staff, Director/Executive

2+ years exp

Pfizer is building an AI-first R&D engine to enhance the discovery and development of medicines. The Director of AI Engineering will work with scientists to develop AI systems that influence drug discovery decisions and will mentor others to foster a culture of experimentation and learning.

BiotechnologyHealth CareMedicalPharmaceuticalPrecision Medicine

No H1B

Responsibilities

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

Apply state-of-the-art generative approaches to molecular and protein engineering

Architect robust pipelines with modern MLOps: cloud and HPC environments, distributed training, reproducibility, governance, and observability

Integrate multimodal data—omics, imaging, real-world evidence, and scientific literature—into representations that surface biological insight and guide experimental and clinical strategy

Partner with scientific and strategy leaders to model uncertainty, run scenario analyses, and optimize resource allocation across a complex R&D portfolio

Continuously assess emerging AI methods and tools, translating advances into practical, defensible applications for a specific R&D discipline

Mentor scientists and engineers, foster hands-on curiosity, and help build a culture where rigorous experimentation and learning are the norm

Publish, present, and engage with the broader AI and life-sciences community at leading conferences and forums

Qualification

AI EngineeringPredictive ModelingGenerative AIMachine Learning SystemsPythonCloud EnvironmentsHPC EnvironmentsScientific CommunicationIntellectual CuriosityCollaboration

Required

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

Demonstrated expertise in predictive modeling, generative AI, and ML system design

Strong programming skills in Python and modern ML frameworks (e.g., PyTorch, TensorFlow), plus experience scaling models in cloud and/or HPC environments

Proven ability to collaborate with other scientists, and could include laboratory bench researchers, clinicians, product teams, and business leaders

Clear scientific communication, intellectual curiosity, and a mission-driven mindset focused on improving patient outcomes

Preferred

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 forecasting, or portfolio analytics

Comfort operating across disciplines—chemistry, biology, pharmacology, statistics—with the ability to ground models in biological and clinical reality