We are Ads Core ML Team from TikTok Global Monetization Technology. Our core mission is to generate revenue and optimize the user experience on the world's leading short-form video platform.
Within the global monetization and advertising landscape, we are responsible for accelerating TikTok revenue growth through advanced machine learning technology and delivering optimal user experience solutions.
You will be part of a team that's optimizing ads format and ranking strategies, and you will be responsible for bringing a better return on investment for advertisers.
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.
Responsibilities:
- Research and develop a cutting-edge global advertising delivery system using advanced technologies, including ML/DL, RL, LLM, and scaling laws in ad recommendation.
- Optimize efficiency across the entire advertising funnel, focusing on Recall & Rough-sort, Fine-sort (CTR/CVR), format/creative personalization, and system resource allocation.
- Design and establish system frameworks and standards to continuously enhance modeling efficiency.
- Build and manage the delivery pipeline, encompassing modeling, bidding, traffic strategy, and optimizing organic video recommendation systems to meet diverse vertical business needs.
- Drive the influence of user experience and revenue targets, establishing the team as a core pillar within the organization.
Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
- Solid programming skills, proficient in C/C++ and Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment.
- Good theoretical grounding in machine learning/deep learning/algorithm 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.
- Good analytical thinking and critical thinking capabilities.
Preferred Qualifications:
- Participation in national math/coding competitions (e.g., ACM, Hacker Cup, Hash Code, USACO, IOI, CCPC, etc.).
- Strong understanding of key domains in online advertising systems, including ads bidding & auction, ads quality control, and related concepts such as CPC/CPM, CTR/CVR, Ranking/Targeting, Conversion/Budget, Campaign/Creative, Demand/Inventory, DSP/RTB.
- Experience with resource management and task scheduling in large-scale distributed software environments (e.g., Spark, TensorFlow).
- Published papers/citations or conducted paper reviews in areas such as NLP, CV, and Recommender Systems (e.g., RecSys, KDD, ICML, CVPR, NeurIPS).
- Familiarity with advanced techniques like LLM, reinforcement learning, transfer learning, and counterfactual optimization is a plus.