Key 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. 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.