Responsibility
Lead research and development of core AI technologies, including machine learning algorithm design, deep learning model optimization, large-scale model training and deployment, and AI system architecture design.
Explore application scenarios of AI technologies in vertical domains (e.g., 5/6G, Web3.0, education, healthcare, legal, finance, AI for Science, etc.), design end-to-end solutions, and promote their validation and implementation.
Drive the development of AI platforms and tools, covering full-process technical challenges such as data governance, model training, inference acceleration, and automated evaluation.
Foster cross-team collaboration, promote industry-academia partnerships, contribute to open-source communities, publish academic papers, and facilitate patent portfolio development.
Complete other tasks assigned by leadership.
Requirements
Full-time Ph.D. in Computer Science, Mathematics, Statistics, Artificial Intelligence, Data Science, or related fields; experience contributing to open-source communities is preferred.
Solid theoretical foundation in machine learning and deep learning, familiarity with mainstream algorithms and models (e.g., CNN/RNN/Transformer/GAN). Experience in computer vision (CV), natural language processing (NLP), large models, or reinforcement learning is preferred.
Proficient in frameworks such as TensorFlow/PyTorch, skilled in programming languages like Python/C++, and experienced in engineering practices such as distributed training, model compression, and edge-side inference. Experience in developing large models (e.g., LLM, multimodal models) is preferred.
Practical experience in combining AI with vertical domains (e.g., 5G/6G, Web3.0, education, healthcare, legal, finance, AI4Science) is preferred.
Excellent communication and coordination skills, fluent in English (listening, speaking, reading, and writing), capable of conducting daily research work in English.
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