Director, Research Commercialization (AI Blackbelts)

Google•Sunnyvale, CA

7d•$307,000 - $427,000

About The Position

The Google Cloud AI Blackbelt team is an external-facing incubation engine within the Applied AI Engineering (AAIE) organization. Our primary mission is to bridge the gap between Google's cutting-edge frontier research (including DeepMind) and enterprise production reality. Within this organization, the AI for Science domain represents one of our most critical key bets. This specialized unit focuses on accelerating scientific discovery and R&D by bringing Google’s frontier scientific models (e.g., AlphaFold, specialized LLMs for genomics, computational chemistry, and material science) to the world's leading life sciences, healthcare, and industrial research organizations. We operate under a "strategic AI co-engineering" model, building complex scientific AI solutions directly alongside our customers' R&D and engineering teams. As the Director of AI for Science, you will lead a highly specialized, global team of AI researchers, computational scientists, and forward-looking software engineers. You will be responsible for defining and executing the technical go-to-market and incubation strategy for our scientific AI portfolio. You will act as the ultimate bridge between Google’s scientific research breakthroughs and commercial enterprise application. Serving as a trusted technical and scientific advisor to Chief Science Officers (CSOs) and R&D leaders at our "Lighthouse" accounts, you will drive the co-engineering of platforms that accelerate drug discovery, molecular simulation, and advanced scientific modeling. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $307000 - $427000 (USD) + 30% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].

Requirements

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).

Nice To Haves

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.

Responsibilities

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.