Set the technical vision and multi-year strategy for Ads Quality machine learning, connecting model innovation to Pinner value, advertiser performance, revenue, and marketplace health.
Define technical strategy and investment priorities across Interfaces, Ingestion, User Match, and Conversion Quality. Lead managers and senior technical leaders through a portfolio of multi-year ML and data-platform investments.
Set the technical vision and roadmap for the Pinterest AI Platform, aligning with business priorities and long-term company strategy. Drive cross-functional strategic initiatives that advance our platform capabilities across:
Lead the strategy and technical direction of Pinterest’s data infrastructure for big data and AI applications.
Product Led Growth: Drive the adoption, retention, and revenue impact of our suite of business-facing tools through intelligent product investments.
Contribute to cutting-edge AI/ML research that can be applied to Pinterest challenges across Ads, Discovery, Growth, and Search. Identify, scope, architect, build, and deliver full, novel ML solutions from 0 to 1 at scale. Identify timelines and obtain resources needed.
Vision and Strategy: Own the technical roadmap and strategic vision for Pinterest’s next-generation recommendation systems. Champion the use of state-of-the-art ML techniques to deliver revolutionary innovations in recommendation technology.
Set the technical vision and roadmap for model training and serving, with reusable interfaces to data and feature infrastructure. Lead architectures for distributed training, fine-tuning, distillation, evaluation, and high-scale CPU/GPU inference.
Define and drive the long-term technical vision for indexing and retrieval infrastructure across Core, Ads, and Shopping, aligning architecture investments to measurable improvements in freshness, quality, coverage, reliability, and cost efficiency.
Set the technical vision and roadmap for model training and serving across PMLI, with reusable interfaces to data and feature infrastructure. Lead architectures for distributed training, fine-tuning, distillation, evaluation, and high-scale CPU/GPU inference.
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