Senior Scientist, AI‑Enabled Structural Biology

Requirements

• Breadth of experience across multiple target classes, such as GPCRs, transporters, ion channels, dynamic protein complexes, or difficult soluble targets in structural biology or protein engineering.
• Familiarity with pharmacology, biochemistry, or biophysical assays used to generate and interpret data supporting construct integrity and functional relevance.
• Experience developing scalable or generalizable experimental strategies that extend beyond individual projects and

Additional Information

Job Description & Responsibilities Pfizer is seeking a Senior Scientist in AI-Enabled Structural Biology to advance structure-based discovery by integrating AI-guided protein design, structure prediction, and cryo-electron microscopy (cryo-EM), including AI/ML-enabled data analysis. This role is intended for a scientist who can apply and adapt state-of-the-art protein design methodologies to translate computational approaches into experimentally tractable solutions and leverage AI/ML approaches to accelerate structural discovery. In particular, the candidate will utilize de novo designed proteins, engineered constructs, scaffolds, and conformational stabilizers to expand structural access and mechanistic understanding of challenging targets for structure-based drug design (SBDD) across diverse modalities, including small molecules, biologics, and vaccine antigens. The successful candidate will operate at the interface of structural biology, AI-driven discovery, and mechanism-focused validation, leading iterative design-test-learn workflows from computational design through experimental validation and structural interpretation. This includes executing and adapting computational workflows for protein design and cryo-EM data processing and analysis in HPC environments. Working within Structural Biology and in close collaboration with AI/ML, broader Medicine Design partners, and Research Units, this role will contribute to project delivery and the development of scalable, AI‑enabled structural biology and SBDD capabilities across the discovery portfolio. Key responsibilities include: Apply and adapt generative AI and computational protein design methodologies (e.g., AlphaFold, RoseTTAFold, RFdiffusion, ProteinMPNN) to design, optimize, and engineer custom structural tools - including de novo binders, fusion proteins, and fiducials - to enable structurally challenging cryo-EM targets. Execute and adapt computational protein design workflows in HPC environments, including data preparation, job execution, and integration into iterative design-test-learn cycles, with an emphasis on scalable, high-throughput applications for large-scale screening and discovery. Own end-to-end structural biology strategies, including construct engineering, sample preparation, hands-on cryo-EM structure determination, and mechanistic interpretation of complex systems. Apply and adapt AI/ML-enabled approaches in cryo-EM data processing and model building workflows; develop or implement automated, scalable pipelines to support analysis of large and complex datasets. Establish integrated data and analysis workflows that connect computational design with experimental outcomes, leveraging high-quality datasets to inform, validate, and refine next-generation AI/ML models in collaboration with experimental and computational partners. Communicate scientific findings to drive data-informed decisions and project progression, and to build reusable capabilities that extend across discovery programs . Required Qualifications Ph.D. in Structural Biology, Biochemistry, Biophysics, Computational Biology, or a closely related discipline, with a proven track record of innovation in protein design and engineering during their Ph.D. or postdoctoral experience. Hands-on expertise in cryo-EM, including experience with data processing workflows and familiarity with AI/ML-enabled approaches, with an interest in automation and scalable data analysis. Proficiency with current structure prediction and protein design software (e.g., AlphaFold, RoseTTAFold) and familiarity with state-of-the-art generative models (e.g., protein language models, diffusion models like RFdiffusion). Experience working with HPC environments to execute and adapt computational protein design and cryo-EM workflows at scale, including data preparation and job execution, and using scripting (e.g., Python) to automate and operationalize these workflows for high-throughput, large-scale screening. Strong experience in experimental triage for challenging or conformationally heterogeneous systems, with demonstrated ability to integrate structural and functional data to evaluate construct or binder performance and mechanistic impact. Proven ability to work effectively in cross‑functional, multidisciplinary teams, with strong written and verbal communication skills and a track record of presenting complex scientific findings to diverse audiences and contributing to scientific reports and publications.

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Company Intel

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