This job listing expired on Oct 20, 2020
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Tencent Games Global Publishing Center Tech Team is the international unit of Tencent Games, a leading global platform for game development, publishing and operation. Aiming to improve our games and transform the gaming industry, our mission is to solve the toughest challenges in gaming with technology.

With offices in Singapore and around the world, our growth strategy is to build upon attracting the best talent and creating an amazing work atmosphere that balances the energy of a start-up with the resources of a global innovation leader. As the world’s leading technology company, we maintain an entrepreneurial spirit and an open mindset.

If you are passionate about the gaming industry and eager to do groundbreaking work in a friendly, cross-cultural environment, we can provide unparalleled stability, resources, access to more than a billion players, and an international perspective.

If you like us are ambitious and self-driven, we invite you to explore Tencent Games Global Publishing Center Tech Team and take challenges that will create new adventures for billions of players.

Responsibility：

  • Develop and apply machine learning algorithms, capable to follow up latest research directions.
  • Based on oversea (out of China) game data mining and recommendation requirement, determine appropriate methodologies and solutions and develop tools accordingly.

Qualification：

  • MS/PhD in Computer Science, mathematics, statistics or other related fields.
  • 3+ years' experience in data mining and machine learning.
  • Experienced in one the of the following fields: Recommendation, Natural Language Processing or Computer Vision is required.
  • Experienced with computational advertisement, e-Commence recommendation, news-recommendation, or user management is preferred.
  • Familiar with python/C++/scala/SQL/R and experienced with large-scale data analysis frameworks such as Hadoop or Spark is preferred.
  • Familiar with deep learning applications and experienced with deep learning frameworks such as Tensorflow, Caffe, PyTorch or Mxnet is preferred.
  • Publications in Tier 1 conferences/journals, such as KDD/ICML/NIPS/ICLR/IJCAI is preferred.