What the Role Entails

1. Responsible for the application and implementation of AIGC (Generative AI) technology in the gaming field, including NPC dialogue generation, game environment construction, quest plot design, character modeling, animation generation, level design, character behavior modeling, intelligent NPCs, game music, etc.

2. Responsible for the research and development of text generation (e.g., ChatGPT, GPT4), image generation (e.g., Diffusion, GAN, VAE), sound, and video generation technologies in the gaming field, researching techniques such as style transfer, generative adversarial networks, and conditional GANs to achieve personalized and diverse gaming experiences, and efficiently integrating algorithms into game content production processes and real-time rendering.

3. Responsible for the construction of large model evidence-based data analysis and reasoning capabilities, improving model architecture and optimizing training algorithms to enable models to perform more accurate data-based reasoning and analysis, providing solid data support for game business decisions. Build and improve the evaluation system for evidence-based data analysis and reasoning, quantitatively assess model performance, promptly identify problems, propose targeted solutions, and continuously optimize model reasoning capabilities.

4. Explore cutting-edge AIGC technology research, continuously pay attention to the latest developments in large industry models, and make technological breakthroughs based on future industry needs to achieve short-term and long-term business value; at the same time, closely monitor the ethical and safe application of AIGC technology.

Who We Look For

1. Current master's or doctoral students graduating in 2026 and 2027, majoring in computer science, mathematics, statistics, artificial intelligence, or related fields. Rich gaming experience is a plus.

2. Deep understanding of the basic principles and algorithms of machine learning and deep learning, such as neural networks, decision trees, support vector machines, reinforcement learning, and possess certain algorithm practical experience. Experience in publishing papers at top conferences in fields such as ACL, EMNLP, CVPR, NIPS, ICLR, AAAI, KDD is a plus.

3. Proficient in Python programming language, familiar with common machine learning and deep learning frameworks such as TensorFlow, PyTorch, and capable of independent model development and training. Familiarity with the basic principles and usage of game engines (e.g., Unity, Unreal Engine) is a plus.

4. Strong passion for large model/agent technology, with experience in large model/agent development.