Machine Learning Engineer – Search Ads Full-time

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.

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

Requirements

Minimum Qualifications
• Experience with one or more general purpose programming languages including but not limited to: Go, C/C++, Python
• Experience with one or more of the following: Machine Learning, Deep Learning, NLP, ranking systems, recommendation systems
• Solid communication and collaboration skills with the ability to work effectively with internal teams in a cross-functional environment

Preferred Qualifications
• 1+ years of experience in and a good understanding of one of the following domains: Search, Ads, Recommendation, Monetization Products, Shopping Products.