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康明斯招聘深度学习实习生-自然语言处理
摘要:
康明斯 - 世界级的发动机制造商作为全球动力领导者,康明斯设计、生产、分销多元的动力解决方案,并提供服务支持。公司产品囊括柴油及天然气发动机、混合动力和电动动力平台以及相关技术,如:电池系统、燃油系统...

薪资待遇: 135元/天

从业经验: 无需经验

招聘数量: 若干

学历要求: 本科

公司性质: 外资

所在行业: 汽车

公司规模: 1000-5000人

工作地点: 北京市朝阳区广顺南大街8号院3号楼H区7层

招聘启事

康明斯欢迎最新从事过深度学习工程师等行业的优秀人才加入我们,康明斯(中国)投资有限公司将为您提供广阔的发展平台!
岗位信息

Description:

  • Research and development of natural language processing algorithms (text classification, semantic segmentation, entity recognition, etc.)
  • Apply natural language processing, deep learning and other domain algorithms to real-world scenarios, such as knowledge graphs, question and answer systems, recommendation systems, etc.
  • Optimize our current models. Troubleshoot and debug model. Help engineering team integrate it into the production application.
  • Assess needs and recommends experiments, data, and projects, suggests new algorithmic development, drive tactical decisions about approaches and needed data.
  • Use deep learning and machine learning algorithms, and advanced mathematical models to deliver insight, recommendations and solutions.
  • Develop sustainable, consumable, accurate, and impactful reporting on model inputs, model outputs observed outputs and Key Performance Indicators (KPIs).
  • Present compelling, validated stories to team members and stakeholders throughout and at the completion of the internship.



Qualification & Experience:

  • Undergraduate or Master in Statistics, Computer Science, Data Science, Mathematics or Auto Engineering or closely related STEM fields.
  • Good self-learning, analytical, writing and communication capability. Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline.
  • Demonstrated proficiency in deep learning and machine learning algorithms and concepts.
  • Familiarity with Python NLP/ML tools: Bert、Transformer、Attention、CRF、Sklearn、Numpy and at least one deep learning package; (Tensorflow/PyTorch/Keras).
  • Experience in developing, training and evaluating deep learning and machine learning models,- Experience in implementing deep learning architectures for natural language processing.1
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