Staff Software Engineer, Trends Machine Learning Infrastructure

Pinterest Pinterest · Consumer · Seattle, WA · Monetization

Staff Software Engineer focused on building and scaling an AI-powered insights platform for advertisers, involving LLM capabilities, data pipelines, and cross-functional collaboration.

What you'd actually do

  1. Set the technical direction for a unified, audience-first insights platform that powers Pinterest Trends, Audience Insights, and recommendations embedded across Ads Manager surfaces.
  2. Architect scalable data pipelines and systems that generate reusable, personalized insights from Pinterest's trend, audience, and content signals.
  3. Lead delivery of high-impact roadmap bets such as Trends Digest, Moments, Topics expansion, and Product Attributes from prototype to production.
  4. Build LLM-powered capabilities (summarization, classification, conversational insights, agentic review) with strong safety, quality, and evaluation guardrails.
  5. Partner with Product, Design, Data Science, and Ads org teams to bring proactive, contextual insights into advertiser workflows beyond standalone surfaces.

Skills

Required

  • Designing large-scale data or ML-powered platforms
  • Building tools and data pipelines leveraging AI coding tools
  • Product-engineering counterpart on an AI-first product launch
  • Using AI to accelerate engineering and analysis workflows
  • Validating accuracy, performance, and quality of AI-assisted work
  • Critical evaluation and verification of AI-assisted work
  • Leading cross-surface product integrations

Nice to have

  • Bachelor's degree in Computer Science, a related field, or equivalent experience
  • Mentoring engineers
  • Raising the technical bar
  • Establishing measurement practices

What the JD emphasized

  • significant time designing large-scale data or ML-powered platforms that serve customer-facing products
  • Experience as the product-engineering counterpart on an AI-first product launch from prototype to scale
  • Demonstrated experience using AI to accelerate engineering and analysis workflows, with a clear approach to validating accuracy, performance, and quality.
  • Strong track record of critical evaluation and verification of AI-assisted work — testing, source-checking, data validation, and peer review.
  • Experience leading cross-surface product integrations across multiple teams and organizational boundaries, not just standalone tools.

Other signals

  • building LLM-powered capabilities
  • leading technical evolution of AI-powered insights platform
  • architecting scalable data pipelines for AI