TikTok Ads Core ML Team aims at creating automatic delivery products for the next generation and developing advertising as a global business, instead of just a monetization tool to consolidate the delivery funnel framework allowing multiple teams to iterate parallel. All of our team effort, is to continuously pursue and establish a world-leading ranking model & framework that always benefits our collaborators, users and customers to get better returns.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities:
- Assist in optimizing efficiency across the entire advertising funnel, including Recall&Rough-sort, Fine-sort(CTR/CVR), format/creative personalization and system resource allocation.
- Research & develop a global advanced advertising delivery system through frontier technologies, including ML/DL, RL, LLM and also scaling law in ads recommendation.
- Design & Set up system framework and standard to continuously improve overall efficiency and meet different vertical business needs.
- Work with product and business teams from various scenarios with global impact.
Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD degree in Computer Science, Mathematics, Statistics or a related discipline.
- Solid programming skills, proficient in C/C++ and Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment.
- Good analytical thinking capability. Essential knowledge and skills in statistics.
- Good theoretical grounding in deep learning concepts and techniques.
- Familiar with architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/Pytorch/MXNet), familiar with its architecture and implementation mechanism.
Preferred Qualifications:
- Good knowledge in one of the following fields: Factorization Machine, Uplift Modeling, Diffusion Models, Reinforcement Learning.
- Basic understanding of large recommendation system and ads serving system concepts.
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