Pinterest
Pinterest
1,001 – 5,000 Employees
H1B Visa SponsorB2CMedia
Pinterest is a visual discovery platform where people find inspiration, save ideas, and organize them around interests, projects, and purchases. Its experience combines personalized recommendations and visual search across topics such as home design, fashion, recipes, and events, while also helping creators and retailers reach audiences through commerce tools and advertising.

Machine Learning Engineering Manager II, Content Foundation

Lead Pinterest’s machine learning engineering team building content-understanding systems for Discovery, Search, Growth, and Ads. Set technical direction for scalable LLM and VLM infrastructure while guiding cross-functional delivery.

Description

  • Build, mentor, and expand a team of engineers and applied scientists working on foundational content-understanding signals, infrastructure, and systems for Discovery, Search, Growth, and Ads.
  • Define the long-term technical strategy for semantic signal generation, reusable foundational layers, and scalable model-driven infrastructure.
  • Collaborate with product, data science, and downstream engineering teams to deliver impact across Pinterest.
  • Oversee project delivery and stakeholder communication through roadmap planning, technical decisions, risk management, and progress reporting.
  • Solve complex problems across VLM and LLM technologies, semantic content understanding, reusable captions and summaries, inference throughput, freshness, serving efficiency, downstream impact measurement, and large-scale monitoring, evaluation, and debugging.

Requirements

  • At least 7 years of industry experience, including 2 or more years managing teams.
  • A master’s degree in computer science, computer engineering, or equivalent professional experience.
  • Publications at leading machine learning and data mining conferences such as NeurIPS, ICML, KDD, or SIGIR.
  • Experience training and serving LLM and VLM models with large-scale distributed systems.
  • Experience using distributed ML data-pipeline tools such as Hadoop and Spark.
  • Experience leading ambiguous machine learning initiatives to build, apply, and improve generative AI models for content understanding and recommendation systems.
  • Demonstrated technical leadership across machine learning and data systems.
  • Experience establishing direction for multi-quarter roadmaps covering modeling, data pipelines, and productionization.
  • Strong cross-functional communication and collaboration skills.

Benefits

  • Eligibility for equity compensation.
  • Flexibility in the working model.
  • In-person collaboration one to two times per quarter.
  • Equal-opportunity employment and support for medical or religious accommodations.

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