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Open nowPosted 222 days ago

音频理解算法工程师

acnc6zeentra/index41 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now音频理解算法工程师acnc6zeentra/index · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 222 days ago

The posting

1、负责面向语音、音频及跨模态交互场景的大模型研究与训练，设计统一建模文本、语音、音频、视频等多模态信息的原生模型结构与预训练方法，提升模型在理解、推理、生成与交互中的整体能力； 2、负责语音与音频大模型的后训练技术研究，包括但不限于数据构建与合成、指令微调、可扩展监督、偏好建模、强化学习、推理时优化、对齐与安全机制设计，提升模型智能水平、交互自然度和任务泛化能力； 3、研究原生全模态模型能力，推动模型在实时语音对话、音视频理解、跨模态问答、Agent 语音交互等场景中的落地； 4、参与大规模训练数据体系建设，包括数据采集、清洗、过滤、合成、标注、评测与闭环优化，提升训练数据质量与迭代效率； 5、与训练基础设施、推理部署、产品和应用团队协同，推动模型从研究验证到系统落地，持续优化训练效率、稳定性与效果表现； 6、跟踪语音、音频、多模态与大模型领域前沿进展，推动关键研究方向在内部形成可复现、可扩展、可落地的方法论与技术资产。

1、计算机、人工智能、自动化、电子工程、数学、统计学等相关专业本科及以上学历，硕士/博士优先； 2、对机器学习、深度学习、大模型、多模态理解与生成方向有持续而强烈的研究兴趣，认可技术长期价值，并有志于在前沿模型研究与系统落地方向长期投入； 3、具备扎实的机器学习、概率统计、优化理论和基础算法功底，熟练掌握 Python，具备良好的工程实现能力；熟悉 C/C++ 者优先； 4、熟悉 PyTorch 等主流深度学习框架，具备大规模训练实践经验，熟悉分布式训练、混合精度训练、并行策略、训练性能优化和稳定性问题定位； 5、在以下一个或多个方向有扎实研究或工程积累： 大语言模型 / 多模态大模型预训练与后训练； 语音识别、语音合成、语音对话、音频理解、音乐/音频生成； 强化学习、偏好优化、对齐训练、推理优化； 视觉语言模型、音视频理解、实时多模态交互； 6、理解数据在 foundation model 训练中的关键作用，具备数据构建、合成、过滤、自动标注、评测集设计、评测体系建设等相关经验者优先； 7、具备优秀的问题分析与实验设计能力，能够独立推进复杂研究课题，并能将研究结果转化为稳定可复现的训练方案或系统能力； 8、在 ACL、EMNLP、NAACL、NeurIPS、ICML、ICLR、INTERSPEECH、ICASSP、CVPR 等顶级会议/期刊发表论文者优先；在多模态、语音、机器学习相关高水平竞赛中取得优异成绩者优先； 9、有以下经历者优先： 主导过有影响力的大模型、语音、多模态、RL 项目或论文； 有端到端语音或全模态模型研究与落地经验； 有从 0 到 1 搭建研究方向、技术项目或团队的经验； 有实时语音交互、Agent、多模态产品落地经验； 10、具备良好的英文读写能力、沟通协作能力和自驱力，能够在研究、工程和跨团队协作中高质量推进工作。

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