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Tencent Holdings · Shenzhen

# 混元多模态大模型算法加速工程师（深圳/北京/上海/杭州）

Shenzhen 

Listed on Tencent Holdings’s official careers site. Listed as of October 19, 2026. Check that it is still open and apply there.
[Apply on Tencent Holdings’s careers site →](https://careers.tencent.com/en-us/jobdesc.html?postId=2007768437438439424)

[OpenNash Jobs](https://jobs.opennash.com/) / [Tencent Holdings](https://jobs.opennash.com/companies/tencent-holdings/) / 混元多模态大模型算法加速工程师（深圳/北京/上海/杭州）

## Job description

### 主动跟踪学术界与工业界在图像视频生成式模型、多模态理解模型、语音模型、多模态理解生成统一建模等方向的创新算法研究，攻克Diffusion模型加速、多模态理解模型、语音模型（ASR、TTS、Omini等）、多模态理解生成统一建模加速等技术方向，包括但不限于
- （Attention量化/稀疏加速、蒸馏加速、量化、投机解码、剪枝、KV Cache 压缩等等）；
- 通过分析模型和任务性能瓶颈，设计创新的算法优化方案，提升多模态大模型的推理效率，显著降低端到端延迟；
- 作为算法与框架团队之间的技术桥梁，聚焦于图像理解、视频生成、音频理解生成、视觉多轮交互、实时对话等任务，提升模型在推理端的性能；
- 高效协同框架开发及业务算法团队，确保技术方案落地。撰写高质量的技术文档与实验报告，并组织内部分享，推动团队整体技术认知提升。 Requirements 1.拥有计算机科学、人工智能、电子工程、自动化等相关专业。具备扎实的机器学习/深度学习基础； 2.精通Python编程，熟练掌握PyTorch等深度学习框架。对Transformer、Diffusion等模型架构、计算瓶颈与优化方法论有深刻理解; 具33、图像视频生成、音频理解生成、图像视频编辑/理解、模型压缩（步数蒸馏，轻量化模型，投机解码、KVCache优化、稀疏剪枝等）、生成对齐算法（DPO\RLHF等）。

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Jul 19, 2026
- Last checked: Oct 19, 2026
- Listed for: 92 days
- Location: Shenzhen[Apply on Tencent Holdings’s careers site →](https://careers.tencent.com/en-us/jobdesc.html?postId=2007768437438439424)

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