Director, Research Commercialization (AI Blackbelts), Cloud AI

• Lead, scale, and mentor a specialized team of scientific AI co-engineers and researchers. Define the strategic roadmap for the AI for science incubation practice, aligning closely with DeepMind and Cloud Engineering.
• Serve as the executive technical sponsor for major scientific and R&D accounts. Collaborate with customers to integrate frontier models into their proprietary R&D pipelines.
• Act as the critical feedback loop between external scientific enterprises and Google's internal research teams. Translate early-adopter friction in scientific use cases into actionable insights that inform the commercialization of our scientific AI portfolio.
• Drive the technical incubation and adoption of scientific bets to achieve significant market validation and ARR milestones.
• Oversee the creation of production-ready reference architectures, scientific AI "recipes," and best practices to enable the broader Google Cloud Go-To-Market teams in the life sciences and healthcare verticals.

Minimum qualifications:

• 15 years of experience in a technical field bridging software engineering/AI and a scientific domain (e.g., computational biology, cheminformatics, physics).
• 7 years of experience managing and scaling specialized engineering, research, or technical incubation teams.
• Experience with modern machine learning and Generative AI applied to scientific data (e.g., Graph Neural Networks, protein folding models, molecular dynamics, genomics).

Preferred qualifications:

• PhD in Computational Biology, Chemistry, Physics, Bioinformatics, Artificial Intelligence, or a related quantitative field.
• Experience operating in a "strategic AI co-engineering" or highly technical incubation environment, building with external customers.
• Familiarity with the complex regulatory, security, and data governance requirements inherent in scientific and healthcare enterprise environments.
• Proven track record of engaging with C-level executives (CSO, CIO, Head of R&D) and driving complex technical transformations in scientific or highly regulated industries (e.g., Pharma, Life Sciences).
• Strong reputation in the scientific AI community, with published research in journals, patents, or contributions to open-source scientific computing projects.

• 15 years of experience in a technical field bridging software engineering/AI and a scientific domain (e.g., computational biology, cheminformatics, physics).
• 7 years of experience managing and scaling specialized engineering, research, or technical incubation teams.
• Experience with modern machine learning and Generative AI applied to scientific data (e.g., Graph Neural Networks, protein folding models, molecular dynamics, genomics).