Research and optimize content generation models (text, image, audio, 3D models, etc.) to address challenges such as generation quality, diversity, controllability, and efficiency.
Aim to improve user experience and production effectiveness, and support the productization of algorithms.
Conduct algorithm training and optimization in areas such as image generation, multi-modal large models, and few-shot learning.
Based on inhouse products and business needs, improve the performance and experience of AI painting, text generation, and video generation through: Prompt optimization / Generation model R&D / Adapter development / Performance acceleration which also includes resolving algorithm bottlenecks when applying models in real business scenarios.
Address the industrial deployment of multimodal generative models and actively explore model design and optimization in an R&D context
PhD (preferably fulltime) in Computer Science, Artificial Intelligence, Mathematics, or related fields.
Solid foundation in computer vision or machine learning algorithms; candidates with publications in top conferences or journals are preferred.
Proficient in machine learning and deep learning fundamentals, and familiar with mainstream AIGC frameworks, including GAN, VAE, VQGAN, Diffusion models, etc.
Familiar with generation model extensions such as ControlNet, LoRA, and Text Inversion.
Familiar with multi-modal models like CLIP, ERNIE-ViL, and other transformer-based cross-modal representation models. Hands-on experience in NLP, multi-modal learning, or AI-generated content is a strong plus.
Strong learning ability, clear logical thinking, excellent communication skills, and a high level of curiosity.
Good teamwork and interpersonal communication skills.
Fluency in both English and Mandarin to deal with international stakeholders and stakeholders who are based in HQ
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