Machine Learning Engineer Ii, Core Engineering

Pinterest Pinterest · Consumer · Toronto, ON · Core Engineering

Machine Learning Engineer II at Pinterest focused on building and improving ML models for core product surfaces like Homefeed, Ads, Growth, Shopping, and Search. The role involves developing cutting-edge deep learning and ML technologies for personalization, using data-driven methods, and working with large-scale systems and stream data. The primary output is the improvement and deployment of ML models within the consumer product.

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. Keeping up with industry trends in recommendation systems

Skills

Required

  • 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
  • End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
  • Expertise in scalable realtime systems that process stream data

Nice to have

  • 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.

What the JD emphasized

  • applying machine learning methods
  • large scale machine learning systems
  • scalable realtime systems

Other signals

  • personalization
  • recommendation systems
  • deep learning
  • large-scale machine learning systems