Staff Machine Learning Engineer, Programmatic Ads

PinterestSan Francisco, WA
Hybrid

About The Position

Pinterest is building a new Programmatic Ads ML team to bring in exchange-sourced ads demand and supply. We’re looking for a Staff ML engineer to develop core bidding and ranking systems that help us optimally buy and sell inventory across exchanges, driving strong ROI for advertisers and growing a critical revenue stream for Pinterest.

Requirements

  • Industry experience building and shipping large-scale production ML systems in ads, search, recommendations, or related domains.
  • Deep experience with control/optimization algorithms for bidding, pacing, allocation, or similar marketplace problems.
  • Strength in probabilistic modeling and measurement (e.g., quality/fraud signals, deep-learning engagement prediction) and making principled trade-offs between coverage, accuracy, and impact.
  • Proven Staff-level technical leadership as an IC: driving technical direction and cross-team alignment without formal people management.
  • Demonstrated ability to use AI to improve speed and quality of your workflow, with a strong track record of validating and stress-testing AI-assisted outputs.
  • Degree in Computer Science, Statistics, or a related field.
  • 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.

Responsibilities

  • Design and implement algorithms for real-time bidding, ad scoring/ranking, inventory selection, and yield optimization across multiple exchanges.
  • Own end-to-end ML systems: problem framing, metrics, data/feature design, model training, evaluation, and online experimentation.
  • Introduce and productionize new exchange and supply signals (e.g., quality, conversions, identity, fraud, content understanding) to unlock incremental advertiser value.
  • Partner closely with Ads Ranking & Bidding, Measurement, and Programmatic Engineering to integrate new models and objectives into the ads stack.
  • Use AI to accelerate analysis, experimentation, and iteration (e.g., exploring model variants, automating path from learnings to launch) while applying strong judgment and vision.
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