Role Summary
Director of AI Engineering embedded within Pfizer R&D disciplines to translate complex biology into new therapies supported by AI models. You’ll work shoulder-to-shoulder with leading scientists and clinicians to influence molecules, studies, and patient outcomes, applying AI to drug discovery, development, and clinical strategies. This role is ideal for a rising AI technical leader who thrives at the intersection of AI, biology, and real-world impact.
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 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 trail.
- 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.
Qualifications
- PhD or Master’s in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline.
- 2–5 years of applied AI/ML experience. Life sciences experience is preferred but not required.
- 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 the 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), plus experience scaling models in cloud and/or HPC environments.
- Proven ability to collaborate with other scientists, clinicians, product teams, and business leaders.
- Clear scientific communication, intellectual curiosity, and a mission-driven mindset focused on improving patient outcomes.
Skills
- Strong programming skills in Python and modern ML frameworks (e.g., PyTorch, TensorFlow), with experience in cloud and HPC environments.
- Predictive modeling, generative AI, and ML system design expertise.
- Collaborative communication across multidisciplinary teams.
- Ability to ground AI work in biological and clinical realities and to translate scientific insights into actionable decisions.
Education
- PhD or Master’s in Computer Science, Machine Learning, Computational Biology, Software Engineering, AI, or a related discipline.
Additional Requirements
- Hybrid role requiring on-site presence an average of 2.5 days per week; locations include Kendall Square, Cambridge, MA; Groton, CT; La Jolla, CA; and Bothell/Seattle, WA.
- Relocation assistance may be available based on business needs and eligibility.