Machine Learning Engineer Graduate (Lead Ads) - 2027 Start

关于这个机会

The Lead Ads team empowers TikTok's global monetization (billion-dollar business) via efficiently delivering lead ads on TikTok. Our mission is to push the boundaries of large-scale ads delivery systems and lead the innovations of TikTok's personalized online advertising. 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. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities: - Participate in the development of a large-scale Ads system - Responsible for relevance model and strategy optimization, such as semantic matching models, active learning, text/photo/video multi-model, ranking strategy, etc - Participate in the development and iteration of Ads algorithms by using Machine Learning. - Work on NLP (Natural Language Processing) capability improvement and query understanding, such as query classification, seq2seq, NER (Named Entity Recognition), knowledge graph, bidword optimization, etc - Work on CTR/CVR model estimation accuracy, data analysis, modeling, feature engineering - Research and develop Ads pacing algorithms, ads traffic control, etc - Partner with product managers and product strategy & operation team to define product strategy and features

任职要求

Minimum Qualifications: - Individuals who are completing or have recently completed a Bachelor's or above degree in Computer Science, Computer Engineering, or a related technical discipline. - 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 Qualifications: - Ads system, recommendation, searching, ranking, etc.

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