Verified Job On Employer Career Site Job Summary: Pinterest is a visual bookmarking tool for saving and discovering creative ideas. The Manager II, Machine Learning will lead a team of Machine Learning Engineers to innovate and execute the global Shopping Ads platform, shaping the technical vision for advanced retrieval models and scalable infrastructure. Responsibilities: • 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. Qualifications: Required: • 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. Company: Pinterest is a visual bookmarking tool for saving and discovering creative ideas. Founded in 2010, the company is headquartered in San Francisco, California, USA, with a team of 1001-5000 employees. The company is currently Public Company.