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Tencent Holdings · Beijing

# 边缘AI推理研发工程师（深圳）

Beijing 

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=2074084898423095296)

[OpenNash Jobs](https://jobs.opennash.com/) / [Tencent Holdings](https://jobs.opennash.com/companies/tencent-holdings/) / 边缘AI推理研发工程师（深圳）

## Job description

### Responsibilities 推理方向

1.面向低延迟交互场景打造极致低延迟推理系统，通过调度、传输、计算优化，深度优化端到端推理延迟； 2.设计开发一站式 Serverless AI 推理平台，结合边缘云 Serverless 生态支持一站式边缘 AI 应用，提升实时语音交互等低延迟 AI 应用场景的用户体验； 缓存方向： 1.面向低延迟交互场景打造 KVCache 缓存系统，通过 KVCache 卸载、传输、压缩优化，深度优化端到端推理延迟，提升实时语音交互等低延迟 AI 应用场景的用户体验； 2.设计开发边缘推理 KVCache 平台，结合边缘云 Serverless 生态支持一站式边缘 AI 应用，通过 KVCache 复用、分层存储、跨请求共享等技术提升推理性能。

### Requirements 推理方向
- 有 AI 推理在工业上大规模落地的经验，熟悉 LLM 的模型架构和常用的推理加速方法；
- 熟悉 GPU/TPU 架构，并且能根据硬件架构合理设计上层算力调度、推理加速等相关软件栈，充分发挥硬件算力；

### 熟悉 sglang、vLLM、TensorRT-LLM 等常用推理框架，理解框架的设计和推理加速方法，了解异构芯片的适配过程； 缓存方向
- 有 KVCache 优化或 AI 推理在工业上大规模落地的经验，熟悉 LLM 的模型架构和常用的推理加速方法，理解 KVCache 机制对推理性能的影响；
- 熟悉 SGLang、vLLM、TensorRT-LLM 等常用推理框架，理解框架的 KVCache 设计和推理加速方法；
- 熟悉低延迟硬盘IO优化和网络传输优化，包括但不限于Linux异步IO框架、RDMA高性能网络传输协议等。

## 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: Beijing[Apply on Tencent Holdings’s careers site →](https://careers.tencent.com/en-us/jobdesc.html?postId=2074084898423095296)

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