职位详情

职责 ——负责电子商务场景中与文字内容(如产品说明、图片文字、口头广播说明等)相关的风险识别模型 -负责产品/短视频/直播评论、报道等舆情内容的风险挖掘算法,提高平台治理效果 ——构建行业领先的内容识别方法,构建具有对抗行为的文本内容的跨域、多维识别策略 -探索NLP相关前沿技术,并将其应用于电子商务业务场景 -支持生成可扩展和优化的AI/机器学习(ML)模型 -专注于构建用于提取、转换和加载大量实时非结构化数据的算法,以从理论数据科学模型中部署AI/ML解决方案。 -运行实验来测试部署的模型的性能,并识别和解决过程中出现的错误。 -在团队环境中工作,应用公司要求的统计、脚本和编程语言知识。 -与部署模型的相关软件平台合作。 资格 -扎实的自然语言处理算法基础,对文本分类、相似度匹配、对话问答、机器翻译、序列标注、知识图谱、意图理解、词义消歧等领域有深入的理解和实践经验 ——熟悉常用的机器学习和深度学习算法,了解基本的网络模型结构(DNN/LSTM/CNN等)和文本表示方法(LDA/Word2Vec/ELMo/GPT/BERT等),对深度学习训练和推理模型调优有实际经验 -动手能力强,熟练使用至少一个主流深度学习框架(TensorFlow/PyTorch/Caffe/MXNet),了解分布式训练、提炼加速等实现方法 Responsibilities - Responsible for the risk identification model related to the text content (such as product description, picture text, oral broadcast description, etc.) in the e-commerce scene - Responsible for product/short video/live comments, reports and other public opinion content risk mining algorithm, improve the platform governance effect - Build an industry-leading content recognition method, and build a cross-domain and multi-dimensional recognition strategy for text content with adversarial behavior - Explore NLP-related cutting-edge technologies and apply them to e-commerce business scenarios - Support the production of scalable and optimised AI/machine learning (ML) models - Focus on building algorithms for the extraction, transformation and loading of large volumes of realtime, unstructured data to deploy AI/ML solutions from theoretical data science models. - Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process. - Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm. - Work with the relevant software platforms in which the models are deployed. Qualifications - Solid foundation of NLP algorithm, in-depth understanding and practical experience in text classification, similarity matching, dialogue question and answer, machine translation, sequence tagging, Knowledge Graph, intention understanding, word meaning disambiguation and other fields - Familiar with commonly used machine learning and deep learning algorithms, understand the basic network model structure (DNN/LSTM/CNN, etc.) and text representation methods (LDA/Word2Vec/ELMo/GPT/BERT, etc.), and have practical experience in deep learning training and reasoning model tuning - Strong hands-on ability, proficient in using at least one mainstream deep learning framework (TensorFlow/PyTorch/Caffe/MXNet), and understanding of distributed training, distillation acceleration and other implementation methods - Excellent problem analysis and problem solving skills, have certain processing methods and optimization experience on domain migration, small sample construction, text mining, unsupervised/semi-supervised and other issues - Master basic big data related components (Hadoop/Spark/Hive/Flink), and have experience in large-scale text data processing and cleaning - Familiar with Linux development environment, proficient in C++ /go/python at least one programming language, and solid foundation in algorithms and data structures - Good sense of teamwork and communication skills, practical experience in relevant business scenarios is preferred - Published papers in high-level computer science conferences (ACL, EMNLP, NIPS, AAAI, etc.) or have competition experience are preferred

工作地址

北京海淀黄庄[地铁站]

公司信息

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