Applied Scientist - Monetization Technology - Global Tech Research Program - 2027 Start (PhD)

TikTok Inc

  • San Jose, CA
  • 30+ days ago

    Highlights

    Our research covers cutting-edge directions, including Large Recommender Model scaling laws, end-to-end unified modeling, generative full-link technologies (retrieval, ranking, AIGC material generation, bidding), intelligent advertising placement agents, ultra-long sequence modeling, and causal inference. Team Introduction: Global Monetization Product and Technology team are building the next-generation monetization platforms to help millions of customers grow their businesses, utilizing our products like TikTok.

    Numbers & Facts

    LocationSan Jose, CA

    Description

    Team Introduction: Global Monetization Product and Technology team are building the next-generation monetization platforms to help millions of customers grow their businesses, utilizing our products like TikTok. Our team develops a wide variety of advertisements for numerous uses including feeds, live streaming, branding, measurement, targeting, search, vertical solutions, creative solutions, and business integrity.

    We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

    Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.

    Topic Content: This topic dives deep into TikTok's core global advertising scenarios, driving innovation and implementation of the cutting-edge generative technologies in search, recommendation, and advertising. By deeply integrating foundation models with the advertising business, we address key technical challenges in Large Recommender Models and Large Language Models (LLMs) to build a next-generation intelligent advertising engine with autonomous decision-making capabilities.

    Our research covers cutting-edge directions, including Large Recommender Model scaling laws, end-to-end unified modeling, generative full-link technologies (retrieval, ranking, AIGC material generation, bidding), intelligent advertising placement agents, ultra-long sequence modeling, and causal inference. We tackle extreme challenges of trillion-level features and millisecond responses, advancing advertising recommendation toward the foundation model paradigm to achieve dual improvements in monetization efficiency and user experience. Minimum Qualifications:

    1. Individuals who are completing or have recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
    2. Modeling experience in one or more of the areas: Ads, Search engine, Recommender System, NLP/CV.
    3. Have a solid foundation in algorithms related to LLMs, including but not limited to comprehensive learning and practical experience in areas such as single-modal LLM application and deployment.
    4. Strong publications record in top conferences (e.g., ICLR, NeurIPS, ICML, ACL, EMNLP, N CVPR, ICCV, and ECCV)

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