Machine Learning Engineer, Monetization Engineering

Pinterest Pinterest · Consumer · San Francisco, CA · Monetization

Machine Learning Engineer focused on Monetization at Pinterest, building and improving ML models for various product surfaces like Homefeed, Ads, Growth, Shopping, and Search. The role involves developing cutting-edge deep learning and ML technology for personalization, leveraging data for candidate retrieval, and enhancing content understanding with LLMs. Experience with large-scale recommender systems, ads ranking, and big data technologies is required.

What you'd actually do

  1. Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
  2. Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
  3. Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
  4. Work in a high-impact environment with quick experimentation and product launches
  5. Keep up with industry trends in recommendation systems
  6. Leverage LLMs to enhance content understanding

Skills

Required

  • machine learning methods
  • user modeling
  • personalization
  • recommender systems
  • search
  • ranking
  • natural language processing
  • reinforcement learning
  • graph representation learning
  • data processing pipelines
  • large scale machine learning systems
  • big data technologies
  • Hadoop
  • Spark
  • large scale recommender systems
  • modern ads ranking
  • retrieval
  • targeting
  • marketplace systems

Nice to have

  • Publications at top ML conferences
  • 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
  • Expertise in scalable realtime systems that process stream data
  • Passion for applied ML and the Pinterest product
  • Background in computational advertising

What the JD emphasized

  • end-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
  • Practical knowledge of large scale recommender systems, or modern ads ranking, retrieval, targeting, marketplace systems

Other signals

  • recommendation systems
  • personalization
  • ads ranking
  • LLMs