Machine Learning Engineer, Ads Ranking - USDS job opportunity at Tiktok.

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Tiktok Machine Learning Engineer, Ads Ranking - USDS
Experience: 1-year
Pattern: full-time
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Ads Ranking team

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degreeMaster's (M.Sc.)
loacation SAN JOSE, CA, United States Of America
loacation SAN JOSE, CA....United States Of America

Responsibilities Team Intro: TikTok #Ads #Ranking team is dedicated to model optimization in the ads delivery system. Our goal is to improve monetization efficiency of TikTok Ads products through model optimization in full funnel. The team also aims to design and implement universal modeling solutions, and solve the long-standing problems of ranking algorithms. At TikTok Ads Ranking team, you can optimize model in recall / rough sort / fine sort, and explore innovative algorithms to break through the ceiling of ads performance. In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time. Responsibilities: Responsible for the development of ads delivery and ranking systems for the global market. Optimizing model in ads delivery system: feature engineering, model structure, auto crossing, ads cold start, modeling delayed feedback, multi-task learning, sequence modeling. #Algorithm and system #co-design: retrieval algorithm, sample mining, long sequence; Exploring large-scale distributed training framework: GPU tuning, feature processing, synchronous training. Improving ads delivery efficiency in privacy-preserving environments; LRM, the next generation rec system using LLM learning paradigm: entity understanding, end2end Generative Recommendation, sequence only based Recommendation, Mixture of Export. Familiar with the entire software development life cycle, including product discussion, requirement analysis, document writing, system design, coding, testing, etc. Using cutting-edge information retrieval and machine learning technologies, develop industry-leading ranking algorithms that directly enhance the real experience of billions of global users. Deeply explore how large-scale models will disrupt the next generation of search, recommendation, and ranking models, fully leveraging the world knowledge embedded in these models to bring revolutionary transformations to search and recommendation services.

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