Senior Researcher, Natural Language Processing

Business UnitLIGHTSPEED 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 TeamLightspeed 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

1. Responsible for NLP research and development including LLM reasoning, conversational agents, machine translation, part-of-speech tagging, sentence analysis and named entity recognition.

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

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

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

Who We Look For

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. Practical experience working with large language models (e.g., DeepSeel, Qwen, LLaMA), including model fine-tuning, instruction tuning, and evaluation. Familiarity with training optimization frameworks like DeepSpeed, FSDP, and LoRA is highly preferred.

4. Experience using tools and frameworks such as LLaMAFactory, Hugging Face Transformers, LangChain, or OpenLLM for model deployment, serving, or pipeline development. Comfortable with using modern MLOps practices for NLP workflows.

5. Demonstrated ability to publish research results in top-tier NLP conferences (e.g., ACL, EMNLP, NAACL, NeurIPS, ICLR) or practical contributions to open-source LLM/NLP projects is preferred.

6. Comfortable with both classical (e.g., CRF, SVM) and deep learning approaches (e.g., Transformers, RNNs, CNNs, LSTMs, GANs). Deep understanding of transformer architectures, attention mechanisms, and distributed training techniques is a strong advantage.

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Equal Employment Opportunity at Tencent

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