Manager II, Machine Learning - Ads Retrieval

Lead and empower a team of talented Machine Learning Engineers within the Ads Retrieval Team at Pinterest, driving the innovation and execution of our global Shopping Ads platform. As a Manager II, Machine Learning, you will be instrumental in shaping the technical vision and strategy for the next generation of ads retrieval models and scalable infrastructure. You will guide your team in pioneering advancements across cutting-edge technologies vital to our advertising ecosystem, including Generative Retrieval, User Sequence Modeling, Learning to Rank, and large-scale Approximate Nearest Neighbor (ANN) techniques. You will oversee the team's efforts in tackling 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 leadership opportunity to shape the future of Pinterest Shopping Ads, directly impacting user experience and advertiser success in a unique discovery-driven marketplace.

What you’ll do:

• Lead and mentor a team of Machine Learning Engineers: Provide technical guidance, mentorship, and career development for a team focused on designing, implementing, and scaling next-generation retrieval models for Shopping Ads. Foster a collaborative and high-performing team culture.
• Define and drive the technical vision and strategy for Ads Retrieval: Collaborate with Product, Data Science, and Engineering leadership to establish a clear roadmap for innovation in retrieval models and infrastructure, aligning with the overall Pinterest Shopping Ads strategy.
• Oversee the design and implementation of advanced retrieval models: Guide the team in pioneering advanced architectures beyond traditional approaches, leading the implementation and optimization of Generative Retrieval, User Sequence Modeling, and Learning-to-Rank models to significantly enhance ad relevance, capture user intent, and improve ranking quality.
• Direct the development and optimization of massively scalable and efficient Ads Retrieval infrastructure: Lead the evolution of our next-gen infrastructure, ensuring it can handle a 5 billion+ Shopping Ads index with lightning-fast, cost-effective retrieval through techniques like efficient ANN algorithms, GPU-accelerated systems, and embedding quantization.
• Champion innovation in personalized Shopping Ads recommendations: Steer the team in developing hyper-personalized retrieval models that incorporate user sequence modeling, learning-to-rank, and generative retrieval to surface the most relevant and novel ads, continuously pushing the boundaries of personalization.
• Foster a holistic approach to retrieval excellence: Evaluate and advocate for the integration of cutting-edge technologies, including Large Language Models (LLMs), Generative Retrieval techniques, advanced Sequence Models, and efficient ANN algorithms, to continuously revolutionize Shopping Ads retrieval and enhance relevance, efficiency, and user engagement.
• Collaborate cross-functionally at a leadership level: Partner closely with Product Management, Data Science, and other Engineering teams to holistically improve the user journey, optimize ad performance across all stages of retrieval and ranking, and drive demand-side growth for Shopping Ads, ensuring a balanced approach across different modeling and infrastructure innovations.
• Drive technical decision-making and ensure engineering best practices: Establish and uphold high standards for code quality, system design, and operational excellence within the team.

What we’re looking for:

• MS or PhD in Computer Science, Statistics, or related field with a strong foundation in machine learning and information retrieval, and deep understanding of a range of retrieval modeling techniques.
• 8+ years of industry experience architecting, building, and scaling large-scale production recommendation or search systems, with a significant focus on high-performance retrieval leveraging diverse modeling approaches, including experience leading technical teams.
• 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 a proven track record of optimizing 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 and grow high-performing engineering teams, providing technical vision, guidance, and mentorship. Experience managing complex technical projects across multiple areas of retrieval innovation and driving balanced technological advancements.
• Excellent communication and cross-functional collaboration skills, capable of articulating complex technical visions to both technical and non-technical audiences, building consensus across diverse teams, and influencing at a leadership level, representing a comprehensive understanding of various retrieval technologies.
• Hands-on experience developing and deploying recommendation systems utilizing Generative Retrieval, User Sequence Modeling, and/or Learning-to-Rank techniques, with experience guiding teams in these areas.
• Expertise in computational advertising, particularly within Shopping Ads or e-commerce domains, with a broad understanding of different retrieval modeling paradigms and their impact on business outcomes.
• Proven track record of optimizing GPU-based systems for high-throughput, low-latency retrieval and experience in implementing embedding quantization and other efficiency techniques at scale, with experience leading teams in these efforts.
• Familiarity with a wide range of retrieval efficiency and scaling techniques, including efficient ANN algorithms, token-based retrieval, and embedding quantization, and the ability to guide a team in leveraging these techniques effectively.

In-Office Requirement Statement

We let the type of work you do guide the collaboration style. That means 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 once a month, and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, Seattle.

This position is not eligible for relocation assistance.

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