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. The TikTok Ads Creative & Ecosystem team's mission is to solve the dilemma of producing effective ads at scale by building industry-leading tech solutions for ads creative/landing page understanding, production/generation, and optimization. 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, and TikTok creative exchange.
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 or working in production environments.