Senior Researcher, Natural Language Processing

Business Unit LIGHTSPEED STUDIOS is made up of passionate players who advance the art & science of game development through great stories, great gameplay, and advanced technology. We are focused on bringing next generation experiences to gamers who want to enjoy them anywhere, anytime, across multiple genres and devices.

About the Hiring Team Lightspeed Tech Center is a R&D department under Lightspeed Studios which develop PUBG Mobile and other high-quality games. Our Tech Center leads the research, exploration, and discovery of innovative technologies and provides technical services for all games during all phases of life cycle, including engine, audio, QA, AI, next generation game, technical cooperation, etc.

What the Role Entails

Responsible for NLP research and development including Chinese word segmentation, part-of-speech tagging, sentence analysis, and named entity recognition.

Responsible for text classification, sentiment analysis, data mining, text generation, topic analysis, label extraction, and other research and development job.

Optimize existing online algorithms, develop efficient and reliable NLP solutions by combining business needs and data.

Explore applications of NLP techniques and deep learning algorithms in games.

Follow the latest developments in academia and industry and quickly apply findings in your work.

Who We Look For

Familiar with NLP fundamentals, with a strong understanding of statistical models, related machine learning principles, and experience working on NLP related projects.

Proficient in at least one programming language, familiar with basic data structures and algorithms.

Experience in big data mining, knowledge-graph auto-making, and deep learning experience is preferred.

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.

Publication of academic results in academic journals and conferences in NLP-related fields (such as: EMNLP, ACL) is preferred.

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.