Implement machine learning algorithms at large scales to optimize and improve new user acquisition efficiency, and leverage acquisition signals to improve new user retention across all ranking phases including but not limited to retrieval, ranking, re-ranking and etc; Work cross functionally with product managers, data scientists and product engineers to understand insights, formulate problems, design and refine machine learning algorithms, and communicate results to peers and leaders; Run regular A/B tests, perform analysis and iterate algorithms accordingly; Have a good understanding of end-to-end machine learning systems. Work with infra teams on improving efficiency and stability
Requirements:
Hands-on experience in one or more of the areas: recommender systems, machine learning, deep learning, pattern recognition, data mining, computer vision, NLP, causal inference, content understanding or multimodal machine learning; Strong programming skills in Python and/or C/C++, and a deep understanding of data structures and algorithms; Familiar with architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/Pytorch/MXNet); Good communication and teamwork skills, be passionate about learning new techniques and taking on challenging problems
Text:
Machine Learning Engineer, Core Feed Recommendation Implement machine learning algorithms at large scales to optimize and improve new user acquisition efficiency, and leverage acquisition signals to improve new user retention across all ranking phases including but not limited to retrieval, ranking, re-ranking and etc; Work cross functionally with product managers, data scientists and product engineers to understand insights, formulate problems, design and refine machine learning algorithms, and communicate results to peers and leaders; Run regular A/B tests, perform analysis and iterate algorithms accordingly; Have a good understanding of end-to-end machine learning systems. Work with infra teams on improving efficiency and stability Hands-on experience in one or more of the areas: recommender systems, machine learning, deep learning, pattern recognition, data mining, computer vision, NLP, causal inference, content understanding or multimodal machine learning; Strong programming skills in Python and/or C/C++, and a deep understanding of data structures and algorithms; Familiar with architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/Pytorch/MXNet); Good communication and teamwork skills, be passionate about learning new techniques and taking on challenging problems
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