Machine Learning Engineer, Ads Targeting

The Ads Targeting's goal is to help advertisers reach their desired audience and optimize advertisement performance. As a member of the Ads Targeting, Ads Core team, you will apply machine learning models to scale budgets by understanding user interest and intention, and build large-scale foundations for data processing and serving for next-generation ad targeting products. This team is working on a variety of products such as custom audience, interest, behavior, lookalike, auto targeting etc., as well as new innovative features. We are seeking Machine Learning Engineers who can help us to improve our existing delivery system that optimizes for advertisers' true business objectives, i.e. desired user value and effectiveness of ROI. You will have a chance to work with a fully globalized team made up of great engineering talents in different countries, and work closely with cross-functional teams to build proper and relevant connections between users, advertisers, and TikTok. What You'll Do: • Responsible for the development of state-of-the-art applied machine learning projects. • Own key targeting components or strategies in the TikTok ads monetization ecosystem. • Build scalable platforms and pipelines for ads targeting products. • Work with product and business teams on the product vision. Qualifications Minimum Qualifications: • BS/MS degree in Computer Science, Statistics, Operation Research, Applied Mathematics, Physics or similar quantitative fields, with related experience. • Hands-on experience in one or more of the following areas: machine learning, deep learning, statistical models and applied mathematical methods. • Strong coding skills, especially in Python/C++/Go. Experience with high-load systems. • Familiarity with online experimentation and analytics. • Familiarity with big data systems including Hadoop and Spark. • Curiosity towards new technologies and entrepreneurship. Preferred Qualifications: • Experience in reinforcement learning, transfer learning, and counter-factual optimization is a plus. • Understanding of the business value of online advertising. • Experience with user modeling using deep learning methods from large scale datasets. Experience with privacy preserving modeling techniques is a plus. • Experience in developing modern ads ranking/retrieval/targeting systems and recommender systems.