Staff Machine Learning Engineer, Monetization

Pinterest is a platform that inspires creativity and planning for memorable experiences. The Staff Machine Learning Engineer will lead innovation in applied ML technologies within the ads ecosystem, focusing on improving ad ranking and user experience.

Responsibilities:

• Design features and build large-scale machine learning models to improve user ads action prediction with low latency
• Drive innovation in personalized Shopping Ads recommendations through advanced modeling
• Develop new methods for inferring user interests from online and offline activity
• Mine text, visual, user signals to better understand user intention
• Leverage multimodal signals (text, visual, user) to better understand user intent
• Collaborate with product and sales teams to design and implement new ad products
• Partner cross-functionally with product, sales, data science, and engineering teams to design and improve user journey and optimize ads performance across all stages of retrieval and ranking
• Build and improve backend systems and statistical models that underlay the marketplace to maximize value for Pinners, Partners and Pinterest
• Define and implement experiments to understand long term Marketplace effects
• Develop strategies to balance long and short term business objectives
• Drive multi-functional collaboration with peers and partners across the company to improve knowledge of marketplace design and operations

Requirements:

• Degree in Computer Science, Machine Learning, Statistics or related field
• Experience building production machine learning systems at scale, data mining, search, recommendations, and/or natural language processing
• Experience leading projects and mentoring team members
• Strong mathematical skills with knowledge of statistical methods
• Excellent written and verbal communication skills and strong cross-functional collaboration
• Background in computational advertising is a plus, but not required
• Experience using 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