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서울특별시, 대한민국
Onsite
●Research and develop advanced speech synthesis and generation algorithms (e.g., TTS, voice conversion, sound/music generation) based on LLMs, multimodal/omnimodal LLMs.
●Develop and optimize speech and audio synthesis systems for online applications, improving effectiveness, efficiency, and scalability.
●Explore and advance full-duplex/streaming multimodal LLM capabilities in speech understanding, generation, and real-time spoken interaction.
●Collaborate cross-functionally with research and engineering teams from prototyping to production.
岗位职责
● 基于大语言模型（LLM）及多模态/全模态大模型，研究和开发前沿语音合成与生成算法（如 TTS、语音转换、声音/音乐生成等）。
● 开发和优化面向线上应用的语音及音频合成系统，提升效果、效率和可扩展性。
● 探索并推进全双工/流式多模态大模型在语音理解、语音生成及实时语音交互方面的能力。
● 与研究和工程团队跨职能协作，推动项目从原型验证到生产落地。
Requirements
●Ph.D. in Computer Science, Electrical Engineering, Signal Processing, or a closely related field.
●Strong foundation in speech/audio processing and modern generative models (e.g., diffusion, flow matching, autoregressive, codec-based approaches).
●Hands-on experience extending LLMs to speech/audio modalities (e.g., speech tokenizers, multimodal adapters, speech-text joint training). Experience with full-duplex or streaming spoken dialogue systems and real-time interaction modeling is a plus.
●Proficient in Python and deep learning frameworks (e.g., PyTorch); experience with distributed training is a plus.
●Track record of publications at top-tier venues (e.g., ICML, NeurIPS, ICLR, ACL, ICASSP, Interspeech).
●Mandarin Chinese / Business-level Japanese or English
任职要求
● 计算机科学、电子工程、信号处理或相关领域博士学位。
● 具备扎实的语音/音频处理基础，熟悉现代生成模型（如扩散模型、流匹配、自回归模型、基于编解码器的方法等）。
● 具有将大语言模型扩展至语音/音频模态的实际经验（如语音分词器、多模态适配器、语音-文本联合训练等）；有全双工或流式语音对话系统及实时交互建模经验者优先。
● 熟练使用 Python 及深度学习框架（如 PyTorch）；有分布式训练经验者优先。
● 在顶级学术会议发表过论文（如 ICML、NeurIPS、ICLR、ACL、ICASSP、Interspeech 等）。
●中文/商务日语或英文
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