Machine Learning Engineer

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Our Client, a leading destination for short-form mobile video is seeking aMachine Learning Engineer - Search Ads .

The Search Ads team constantly pushes the boundaries of general search engine monetization across our apps, building a globally leading Search Ads monetization system. At the Search Ads team, you will have the chance to work on large-scale distributed storage and architecture, NLP, Rank, and IR related problems. You will be also deeply involved in the innovation and optimization of our Ad format, creative display, and the ROI of ads delivery. We are looking for candidates who brave difficulties, share a passion for tackling complexity and developing our Search Ads product from 0 to 1 with a world-class team of passionate engineers.

What You'll Do:

• 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
  
  

• BS degree in Computer Science, Computer Engineering or other relevant majors.
• Excellent programming, debugging, and optimization skills in general purpose programming languages
• Ability to think critically and to formulate solutions to problems in a clear and concise way. Preferred Qualifications:
• Experience with one or more general purpose programming languages including but not limited to: Go, C/C++, Python.
• Good understanding in one of the following domains: ad fraud detection, risk control, quality control, adversarial engineering, and online advertising systems.
• Good knowledge in one of the following areas: machine learning, deep learning, backend, large-scale systems, data science, full-stack.