Pinterest-posted 9 months ago
$208,145 - $364,254/Yr
Full-time • Senior
San Francisco, CA
Professional, Scientific, and Technical Services

Millions of people across the world come to Pinterest to find new ideas every day. It's where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you'll be challenged to take on work that upholds this mission and pushes Pinterest forward. You'll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet. Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences. Our new progressive work model is called PinFlex, a term that's uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more. The Ads Retrieval Team at Pinterest and be a driving force in shaping the future of our global Shopping Ads platform. As a Staff Machine Learning Engineer, you will lead innovation across a spectrum of cutting-edge technologies vital to our advertising ecosystem. You'll be instrumental in developing the next generation of ads retrieval models and scalable infrastructure, powering discovery for millions of shoppers. Your role will involve pioneering advancements in areas like Generative Retrieval, User Sequence Modeling, Learning to Rank, and large-scale Approximate Nearest Neighbor (ANN) techniques. You'll tackle challenges at immense scale - managing a 5 billion+ shopping ads index - and ensure we leverage the most efficient techniques to deliver exceptional performance. This is a high-impact opportunity to shape the future of Pinterest Shopping Ads, directly impacting user experience and advertiser success in a unique discovery-driven marketplace.

  • Design and implement a diverse portfolio of next-generation retrieval models for Shopping Ads.
  • Pioneer advanced architectures beyond traditional approaches, becoming a leader in implementing and optimizing Generative Retrieval, User Sequence Modeling, and Learning-to-Rank models.
  • Build and optimize massively scalable and efficient Ads Retrieval infrastructure.
  • Lead the evolution of our next-gen infrastructure, capable of handling a 5 billion+ Shopping Ads index.
  • Drive innovation in personalized Shopping Ads recommendations through advanced modeling.
  • Develop hyper-personalized retrieval models that incorporate user sequence modeling.
  • Champion a holistic approach to retrieval excellence.
  • Evaluate and integrate a range of cutting-edge technologies, including Large Language Models (LLMs) and efficient ANN algorithms.
  • Collaborate cross-functionally to optimize the entire Pinterest Shopping Ads ecosystem.
  • MS or PhD in Computer Science, Statistics, or related field with a strong foundation in machine learning and information retrieval.
  • 6+ years of industry experience architecting, building, and scaling large-scale production recommendation or search systems.
  • Deep expertise in recommendation systems, especially large-scale retrieval algorithms and architectures.
  • Mastery of deep learning techniques and proven ability to optimize model performance for complex retrieval tasks.
  • Demonstrated ability to lead complex technical projects across multiple areas of retrieval innovation.
  • Excellent communication and cross-functional collaboration skills.
  • Hands-on experience developing and deploying recommendation systems utilizing Generative Retrieval, User Sequence Modeling, and/or Learning-to-Rank techniques.
  • Expertise in computational advertising, particularly within Shopping Ads or e-commerce domains.
  • Proven track record of optimizing GPU-based systems for high-throughput, low-latency retrieval.
  • Familiarity with a wide range of retrieval efficiency and scaling techniques.
  • Base salary range of $208,145—$364,254 USD.
  • Equity eligibility.
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