About the job
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 excited to grow advertisers' and users' 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. Assist in utilizing algorithms to better understand advertisers, creators, and creatives, improving precision in match-making processes.
2. Contribute to online modeling of large-scale commercial traffic, optimizing the distribution strategy of creatives within the recommendation and ads systems.
3. Help develop allocation strategies for both natural and ads traffic, aimed at increasing both short-term and long-term value for advertisers and creators.
4. Collaborate with senior engineers to implement and test new algorithms that enhance the accuracy of content recommendations.
5. Participate in analyzing large datasets to identify trends and patterns, providing actionable insights for improving the ad targeting strategy.
6. Support the development of performance metrics to track the effectiveness of ad and creative distribution strategies.
7. Conduct experiments to validate new algorithms and strategies, ensuring scalability and efficiency in production systems.
8. Contribute to continuous optimization of machine learning models for better performance across diverse traffic and creative data.
9. Assist in preparing and presenting reports on model performance and recommendations to stakeholders.
Qualifications
Minimum
1. Currently pursuing a Bachelor's degree or higher in Computer Science or a related field.
2. Research/internship experience or coursework in machine learning (e.g., RecSys, NLP, CV, GE), with a preference for candidates with exposure to recommendation systems.
3. Solid understanding of data structures and algorithms, with proficiency in at least one programming language (e.g., Python, C++, Golang).
4. Strong interest in exploring new technologies, with a demonstrated ability to analyze problems and find solutions.
5. Good communication skills, with an eagerness to collaborate within a team and learn from peers.
6. Strong enthusiasm for contributing to business growth and willingness to take on challenges in a dynamic environment.
Preferred
1. Previous internship or research experience in machine learning/deep learning, with a focus on recommendation systems, or advanced ranking solution strategies like RAG/LoRA/MoE etc.
2. Familiarity with large-scale data processing and distributed systems.
3. Exposure to reinforcement learning or deep learning techniques for optimizing recommendation systems.
4. Knowledge of A/B testing and other experimental design techniques to evaluate algorithm performance.
5. Strong interest in content personalization and ad optimization technologies.
6. Experience with model deployment