Manager II Machine Learning - Ads Retrieval
Manager II Machine Learning - Ads Retrieval
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Job Description
Lead and empower a team of talented Machine Learning Engineers within the Ads Retrieval Team at Pinterest driving the innovation and 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 cuttingedge technologies vital to our advertising ecosystem including Generative Retrieval User Sequence Modeling Learning to Rank and largescale Approximate Nearest Neighbor (ANN) techniques. You will oversee the teams 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 highimpact leadership opportunity to shape the future of Pinterest Shopping Ads directly impacting user experience and advertiser success in a unique discoverydriven marketplace.
What youll 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 nextgeneration retrieval models for Shopping Ads. Foster a collaborative and highperforming 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 LearningtoRank 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 nextgen infrastructure ensuring it can handle a 5 billion Shopping Ads index with lightningfast costeffective retrieval through techniques like efficient ANN algorithms GPUaccelerated systems and embedding quantization.
- Champion innovation in personalized Shopping Ads recommendations: Steer the team in developing hyperpersonalized retrieval models that incorporate user sequence modeling learningtorank 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 cuttingedge 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 crossfunctionally 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 demandside growth for Shopping Ads ensuring a balanced approach across different modeling and infrastructure innovations.
- Drive technical decisionmaking and ensure engineering best practices: Establish and uphold high standards for code quality system design and operational excellence within the team.
What were 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 largescale production recommendation or search systems with a significant focus on highperformance retrieval leveraging diverse modeling approaches including experience leading technical teams.
- Deep expertise in recommendation systems especially largescale retrieval algorithms and architectures encompassing Generative Retrieval User Sequence Modeling LearningtoRank and efficient ANN techniques.
- Mastery of deep learning techniques and a proven track record of optimizing model performance for complex retrieval tasks in largescale environments across various model types including generative sequencebased and ranking models.
- Demonstrated ability to lead and grow highperforming 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 crossfunctional collaboration skills capable of articulating complex technical visions to both technical and nontechnical audiences building consensus across diverse teams and influencing at a leadership level representing a comprehensive understanding of various retrieval technologies.
- Handson experience developing and deploying recommendation systems utilizing Generative Retrieval User Sequence Modeling and/or LearningtoRank techniques with experience guiding teams in these areas.
- Expertise in computational advertising particularly within Shopping Ads or ecommerce domains with a broad understanding of different retrieval modeling paradigms and their impact on business outcomes.
- Proven track record of optimizing GPUbased systems for highthroughput lowlatency 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 tokenbased retrieval and embedding quantization and the ability to guide a team in leveraging these techniques effectively.
InOffice Requirement Statement
We let the type of work you do guide the collaboration style. That means were 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 inperson 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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Required Experience:
Manager
Employment Type
Full Time
