Staff Machine Learning Engineer Ads Retrieval
Staff Machine Learning Engineer Ads Retrieval
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Job 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 cuttingedge technologies vital to our advertising ecosystem. Youll 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 largescale Approximate Nearest Neighbor (ANN) techniques. Youll 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 highimpact opportunity to shape the future of Pinterest Shopping Ads directly impacting user experience and advertiser success in a unique discoverydriven marketplace.
What youll do:
- Design and implement a diverse portfolio of nextgeneration retrieval models for Shopping Ads: Pioneer advanced architectures beyond traditional approaches becoming a leader in implementing and optimizing Generative Retrieval User Sequence Modeling and LearningtoRank models to significantly enhance ad relevance capture user intent and improve ranking quality.
- Build and optimize massively scalable and efficient Ads Retrieval infrastructure: Lead the evolution of our nextgen infrastructure capable of handling a 5 billion Shopping Ads index ensuring lightningfast costeffective retrieval through techniques like efficient ANN algorithms GPUaccelerated systems and embedding quantization.
- Drive innovation in personalized Shopping Ads recommendations through advanced modeling: Develop hyperpersonalized retrieval models that incorporate user sequence modeling to deeply understand user shopping journeys and leverage learningtorank to surface the most relevant ads while also exploring the potential of generative retrieval for novel ad discovery.
- Champion a holistic approach to retrieval excellence: Evaluate and integrate a range 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 push the boundaries of relevance efficiency and user engagement.
- Collaborate crossfunctionally to optimize the entire Pinterest Shopping Ads ecosystem: Partner with Product Data Science and 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.
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 expertise across a range of retrieval modeling techniques.
- 6 years of industry experience architecting building and scaling largescale production recommendation or search systems with a focus on highperformance retrieval leveraging diverse modeling approaches.
- 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 proven ability to optimize model performance for complex retrieval tasks in largescale environments across various model types including generative sequencebased 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 fastpaced collaborative environment.
- Excellent communication and crossfunctional collaboration skills capable of articulating complex technical visions and building consensus across diverse teams representing a comprehensive understanding of various retrieval technologies.
- Bonus Points:
- Handson experience developing and deploying recommendation systems utilizing Generative Retrieval User Sequence Modeling and/or LearningtoRank techniques.
- Expertise in computational advertising particularly within Shopping Ads or ecommerce domains with a broad understanding of different retrieval modeling paradigms.
- Proven track record of optimizing GPUbased systems for highthroughput lowlatency retrieval and experience in implementing embedding quantization and other efficiency techniques.
- Familiarity with a wide range of retrieval efficiency and scaling techniques including efficient ANN algorithms tokenbased retrieval and embedding quantization.
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 one time per month and therefore needs to be in a commutable distance from one of the following offices: San Francisco Palo Alto Seattle.
Relocation Statement:
- This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
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Required Experience:
Staff IC
Employment Type
Full-Time
