Staff Machine Learning Engineer, Ads Conversion Core Modeling

Pinterest Pinterest · Consumer · Palo Alto, CA · Monetization

Staff Machine Learning Engineer focused on Ads Conversion Core Modeling at Pinterest. This role involves leading technical direction, designing and building large-scale DNN models for user action prediction, and mining various signals to understand user intent. The engineer will use AI to accelerate workflows and automate tasks, while also mentoring other engineers and collaborating with product and sales teams. Experience with production ML systems at scale, particularly in Search, Recommendations, or Ranking, is required.

What you'd actually do

  1. Lead the technical direction and development of state-of-the-art applied ML projects for ads conversion.
  2. Design and build large-scale DNN models to improve user action prediction with low latency.
  3. Mine text, visual, and user signals to better understand intention and infer interests from online activity.
  4. Use AI to accelerate analysis and iteration, while applying judgment and verification to ensure correctness and quality.
  5. Automate repeatable tasks such as documentation, reporting, and QA checks to speed up the development lifecycle.
  6. Coach and mentor engineers while collaborating with product and sales to design new ad products.

Skills

Required

  • Bachelor's degree in Computer Science, Statistics, or a related field.
  • 6+ years of industry experience building production ML systems at scale (Search, Recommendations, or Ranking).
  • 2+ years of experience leading technical projects or teams.
  • Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.
  • Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
  • Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.
  • High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final deliverables.
  • Strong mathematical foundation and experience with statistical methods and A/B testing.

What the JD emphasized

  • production ML systems at scale
  • AI to accelerate analysis and iteration
  • applying judgment and verification to ensure correctness and quality
  • avoid over-reliance on AI

Other signals

  • Lead the technical direction and development of state-of-the-art applied ML projects for ads conversion.
  • Design and build large-scale DNN models to improve user action prediction with low latency.
  • Mine text, visual, and user signals to better understand intention and infer interests from online activity.
  • Use AI to accelerate analysis and iteration, while applying judgment and verification to ensure correctness and quality.
  • Automate repeatable tasks such as documentation, reporting, and QA checks to speed up the development lifecycle.