P

Pinterest

Runs an app for finding and saving visual ideas

Machine Learning Engineer Manager 2 - Content Foundation

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 7 years of industry experience, including at least 2 years of management experience.
  • A master's degree in computer science or computer engineering, or equivalent work experience.
  • Publication at top machine learning and data mining conferences such as NeurIPS, ICML, KDD, or SIGIR.
  • Experience with large-scale distributed training and inference of large language models and vision-language models.
  • Experience with distributed tooling for machine learning data pipelines, such as Hadoop or Spark.
  • Experience leading ambiguous machine learning projects to build, apply, and improve generative AI models for content understanding and recommendation systems.
  • Proven technical leadership across machine learning and data systems, including setting direction for multi-quarter roadmaps spanning modeling, data pipelines, and productionization, and aligning stakeholders on priorities, trade-offs, and execution plans.
  • Experience building cross-functional partnerships with product, data science, and partner machine learning teams to clarify ambiguous problem spaces, co-create solutions, and drive consensus with senior stakeholders.

Responsibilities

  • Lead, mentor, and grow a team of engineers and applied scientists developing foundational content-understanding signals, infrastructure, and systems for Pinterest products across Discovery, Search, Growth, and Ads.
  • Set a long-term technical vision for semantic signal generation, reusable foundational layers, and scalable model-powered infrastructure, and partner with product, data science, and downstream engineering teams to ensure these signals have meaningful impact.
  • Manage project execution and stakeholder communication, including roadmap planning, technical decision-making, risk mitigation, and progress updates to achieve business goals.
  • Guide the team in using vision-language model and large language model technologies to understand Pinterest content and generate high-quality signals with precision, recall, and coverage across multiple locales.
  • Guide development of reusable foundational layers, including captions, summaries, and semantic descriptors, that efficiently support multiple downstream use cases with low infrastructure cost.
  • Improve inference throughput, freshness, and serving efficiency so content-understanding signals can keep pace with new Pins and evolving content.
  • Measure and improve the impact of content-understanding signals on relevance, personalization, engagement, and ads performance across surfaces.
  • Build robust monitoring, evaluation, and debugging systems to ensure signal quality, accessibility, latency, and freshness at scale.

About the Company

Pinterest

Runs an app for finding and saving visual ideas

Pinterest runs a visual search and discovery app where people look for ideas in areas like cooking, home decor, fashion, and travel, then save the images they like to themed collections called boards. In 2012 it became the fastest standalone website to reach 10 million monthly US visitors, according to measurement firm comScore. Each saved image, called a Pin, links back to its source, such as a recipe or a product page, and people search with words or with a photo to find similar ideas and things to buy. Brands pay to show ads among Pins in feeds and search results, and Pinterest also sells ads on streaming TV. It launched in 2010 and is based in San Francisco, California.

Company size
5,001-10,000
Company stage
IPO
Headquarters
San Francisco, CA
Founded
2010

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