Director of AI Engineering Pfizer R&D
Scorpion Therapeutics, San Diego, California, United States, 92189
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.
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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.
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