mentation, and apply LLM/MLLM and other AIGC technologies to e-commerce and short-video ad content understanding to build a next-gen large model-based commercial intelligence review system.
- Deepen multimodal understanding tech for text, audio, video, live streaming, etc., optimize model decision-making for high-accuracy autonomous risk judgment, and implement interpretable CoT generation for traceable model decisions.
- Explore RL/Agent applications in multimodal review scenarios, track AIGC cutting-edge trends, and deliver algorithm innovation and engineering implementation tailored to commercial business needs.
- Develop dedicated multimodal content understanding models to empower ad intent recognition, intelligent rule retrieval and accurate risk judgment, enhance advertisers' creation experience, reduce non-compliant content non-detection risks, and protect ad ecosystem security.
Qualifications
Minimum Qualification(s)
- Master's degree or above in CS, AI, Mathematics, Statistics or related majors
- Minimum 1+ year of algorithm R&D/project implementation experience in content understanding or AIGC
- Solid ML/DL theoretical foundation, in-depth understanding of MLLM/LLM, CV, NLP, multimodal fusion and Agent technologies; strong mathematical skills, excellent self-learning and problem-solving abilities; project implementation experience
- Proficient in PyTorch/TensorFlow, with hands-on experience in large model training, fine-tuning and inference deployment; excellent engineering capabilities, master Python/C++.
- Familiar with technical principles and applications of multimodal large models; experience in content understanding, ad analysis, multimodal representation learning or intelligent review
- Accredited publications at CS/ML conferences.