Description:
• Join 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.
Requirements:
• MS or PhD in Computer Science, Statistics, or related field with a strong foundation in machine learning and information retrieval, and expertise across a range of retrieval modeling techniques.
• 6+ years of industry experience architecting, building, and scaling large-scale production recommendation or search systems, with a focus on high-performance retrieval leveraging diverse modeling approaches.
• Deep expertise in recommendation systems, especially large-scale retrieval algorithms and architectures, encompassing Generative Retrieval, User Sequence Modeling, Learning-to-Rank, and efficient ANN techniques.
• Mastery of deep learning techniques and proven ability to optimize model performance for complex retrieval tasks in large-scale environments, across various model types including generative, sequence-based, and ranking models.
• Demonstrated ability to lead complex technical projects across multiple areas of retrieval innovation, drive balanced technological advancements, and mentor junior engineers in a fast-paced, collaborative environment.
• Excellent communication and cross-functional collaboration skills, capable of articulating complex technical visions and building consensus across diverse teams, representing a comprehensive understanding of various retrieval technologies.
Benefits:
• We let the type of work you do guide the collaboration style.
• We're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration one time per month, and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, Seattle.
