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Tencent Holdings · Shenzhen

# 混元LLM大模型量化压缩高级算法工程师（北京/深圳/上海）

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=2008479663327956992)

[OpenNash Jobs](https://jobs.opennash.com/) / [Tencent Holdings](https://jobs.opennash.com/companies/tencent-holdings/) / 混元LLM大模型量化压缩高级算法工程师（北京/深圳/上海）

## Job description

### 深度参与LLM大模型压缩加速方案研究，包括不限于
- ；

### 投机采样
- 适配LLM模型架构特性，探索Prefill / RL等场景下优化加速方案；

### 稀疏化
- Sparse Attention，KV-Cache压缩，模型结构剪枝等优化技术，协同框架定制化稀疏方案，提升模型推理性能；

### 量化
- 优化Transformer中 Linear/KV-Cache/Attention量化算法，适配FP8/INT8/NVFP4等量化方案及不同硬件后端。探索极低bit量化训练方案，深度协同硬件联合优化，实现模型体积极致压缩和性能突破，并推动实际业务落地；

### 新技术
- 聚焦长上下文，多轮对话优化等压缩加速优化技术研究；
- 设计可落地的大模型压缩算法及成本优化方案，助力大模型的性能加速，不限于模型结构/软硬协同优化；
- 分析业务性能瓶颈和模型特点，定制化开发大模型压缩优化工具，实现一站式模型压缩-部署的高速推理方案；
- 参与前沿的模型压缩加速算法研究，追踪领域前沿工作，撰写并发表顶会论文。 Requirements
- 熟悉深度学习算法基本原理，熟练掌握PyTorch，具备扎实的Python编程能力与工程实现能力；
- 熟悉主流LLM模型压缩算法体系，并且了解硬件及推理库上对于模型压缩的支持方案；
- 具有扎实的数理基础，熟悉常见的算法和数据结构，具有良好的编程习惯；
- 了解大模型算法基本原理，熟悉常见LLM大模型，有顶会论文发表者优先。

## 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=2008479663327956992)

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