Drive engineering convergence across technical decision spaces, including data preparation, fine-tuning, sampling, and evaluation pipelines for differentially private synthetic data and privacy-preserving ML. Own customer engagement strategy including customer intake, office hours, bi-weekly engagement reviews, and strategic cross-product area integrations. Identify systemic gaps between research platforms and product use cases to scale bespoke engagements into repeatable, self-serve workflows across Google.
Identify technical and operational barriers to adopting privacy tooling (data preparation bottlenecks) and build or coordinate AI-enabled prototypes and workflows to narrow the decision space and guide engineering investments. Lead disciplined portfolio rhythms including weekly infrastructure syncs, bi-weekly customer engagement forums, monthly technical domain reviews, and 6-week planning cycles. Author clear, outcome-focused upward leadership communications, quarterly OKR plans and scoring rationales, customer impact stories, and executive review materials for Research VPs, Directors, and partner product area leaders.
Job description provided by Google.
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