About the job
TikTok commerce ads team holds a strong advertiser focus and a dedication to technical excellence. We work with advertisers from all verticals, such as E-commerce, Retail, Travel etc, to make TikTok an irreplaceable growth channel for businesses of all sizes and a favorite destination for consumers to discover and purchase products, content, and services. We are on the critical path to delivering TikTok's Ads revenue and working on building the next-generation web ads solution for TikTok. 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.
Responsibilities
Build highly scalable machine learning systems and state-of-the-art machine learning models to improve ads ranking quality and optimize advertisers' marketing strategies. Examples include but are not limited to click through rate prediction, conversion rate prediction, intelligent format selection and user journey optimization.
Explore, develop and experiment with new features to improve model accuracy.
Understand ads platform objectives and take full advantage of modern machine learning to improve ads relevance, quality, and quantity delivered to end-users.
Collaborate with Product Managers, Designers, and other disciplines to explore the next generation of shopping experiences on TikTok.
Qualifications
Minimum
BS/MS degree in Computer Science, Computer Engineering, or other relevant majors, with related work experience.
Solid programming skills, including but not limited to: Go, C/C++, Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment.
Good analytical thinking capability. Have essential knowledge and skills in statistics.
Good theoretical grounding in the machine and deep learning concepts and techniques (CNN/RNN/LSTM, etc.).
Familiar with the architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/PyTorch/MXNet), familiar with its architecture and implementation mechanism.
Preferred
Good understanding in one of the following domains: ads bidding & auction, ads quality control, and online advertising systems (familiar with one or more of these terms: CPC/CPM, CTR/CVR, Ranking /Targeting, Conversion/Budget, Campaign/Creative, Demand/Inventory, DSP/RTB).
Experience in resource management and task scheduling with large-scale distributed software (such as Spark and TensorFlow).
Relevant work or research experiences in search and recommendation.