Snabbfakta
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- London
Ansök senast: 2024-08-18
Senior Researcher, Natural Language Processing
Responsibilities:
LightSpeed Studios is one of the world’s most innovative and successful game developers. With team across China, United States, Singapore, Canada, United Kingdom, France, Japan, South Korea, New Zealand, and United Arab Emirates. We are expanding to more countries.Founded in 2008, LightSpeed Studios has created over 50 games across multiple platforms and genres for over 4 billion registered users. It is the co-developer of worldwide hits
PUBG MOBILE, Apex Legends Mobile, and League of Legends: Wild Rift (Chinese Version).
Job Responsibilities
1. Responsible for NLP research and development including Chinese word segmentation, part-of-speech tagging, sentence analysis and named entity recognition.
2. Responsible for text classification, sentiment analysis, data mining, text generation, topic analysis, label extraction and other research and development job.
3. Optimize existing online algorithms, develop efficient and reliable NLP solutions by combining business needs and data.
4. Explore applications of NLP techniques and deep learning algorithms in games.
5. Follow the latest developments in academia and industry and quickly apply findings in your work.
Requirements:
Job Requirements
1. Familiar with NLP fundamentals, with a strong understanding of statistical models, related machine learning principles, and experience working on NLP related projects.
2. Proficient in at least one programming language, familiar with basic data structures and algorithms.
3. Experience in big data mining, knowledge-graph auto-making, and deep learning experience is preferred.
4. Familiar with word segmentation, part-of-speech tagging, entity recognition, sentence analysis, model pre-training. Experience in sentiment analysis, relationship extraction and event extraction is preferred.
5. Publication of academic results in academic journals and conferences in NLP-related fields (such as: EMNLP, ACL) is preferred.
6. Familiar with CRF, SVM and other classic machine learning algorithms and tools, word vectors, RNN, CNN, LSTM, GAN and other deep learning experiences are preferred.
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