Machine Learning Engineer, TikTok Ads Creative & Ecosystem job opportunity at Tiktok.



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Tiktok Machine Learning Engineer, TikTok Ads Creative & Ecosystem
Experience: 3-years
Pattern: full-time
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Salary:
Status:

Ecosystem Team

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degreeBachelor's (B.Sc.)
loacation SAN JOSE, CA, United States Of America
loacation SAN JOSE, CA....United States Of America

Responsibilities A "creative" is the ad (in the form of a short-form video) served to TikTok users, composed of video, background music, call-to-action card, post-click landing page, and other formats that get delivered to users. A quote goes "creativity is the soul of advertising", because a good ad creative is effective, yet difficult to produce, especially at the scale of #TikTok advertising. The TikTok #Ads Creative & #Ecosystem team's mission is to solve the above dilemma, by building industry-leading tech solutions for ads creative/landing page understanding, production/generation, and optimization, to inspire and empower advertisers, creators, and other 3rd parties in the ecosystem to create and deliver the best engaging creative experiences to the end users. Our work is at the core of TikTok and creator monetization. Examples of our team's work include TikTok video editor, AI-powered smart video generation (we are also exploring AIGC), and TikTok creative exchange (a creative marketplace to connect TikTok advertisers with creators or third-party creative agencies). We are user/product oriented and dedicated to technical excellence. We aim to drive and lead the technology renovation in the ads tech and creative industry, powering products and driving values for our clients, creators, and the whole ecosystem. We are looking for ML engineers who are excited to grow their business understanding, build highly scalable and reliable software/infrastructure, partner across functions with global teams, and make big impacts. If you are someone who welcomes challenges, we are eager to have you on the team! Responsibilities: 1. Utilize algorithms to achieve a comprehensive understanding of advertisers, creators and creatives to improve the precision of match-making. 2. Conduct online modeling on billion-level commercial traffic, optimizing the distribution strategy of creatives in recommendation system and ads system. 3. Develop a scientific allocation strategy for both natural and ad traffic, aimed at increasing the short-term and long-term value for both advertisers and creators.

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