Staff Machine Learning Engineer, Programmatic Ads

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.

• 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.

• 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.

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.

• This role will need to be in the office for in-person collaboration 3 times per quarter and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, Seattle.