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Open nowPosted 42 days ago

校招-AI Infra 工程师（大模型推理\RL方向）

nio/campus870 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now校招-AI Infra 工程师（大模型推理\RL方向）nio/campus · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% 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. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 42 days ago

The posting

我们做什么 在这里，你将参与自研大模型推理引擎从 0 到 1 的建设，亲手优化千亿/万亿级参数模型的推理性能，让每一次回答更快、更准、更省算力。 同时，你会基于 Kubernetes（K8s）与云平台，把大模型推理能力做成稳定、弹性、可观测的在线服务，接触真实业务场景，挑战 AI Infra 行业前沿问题。

岗位职责 参与大模型推理引擎核心模块的设计与开发，提升系统吞吐、降低推理延迟与成本；

探索并落地先进的大模型推理加速方案，如量化、KV Cache、PagedAttention、连续批处理、投机解码、分布式推理、算子优化等；

参与推理平台与云平台建设：基于 Kubernetes/K8s、Docker、云原生技术，实现模型部署、弹性扩缩容、灰度发布、多租户资源隔离、监控告警、日志追踪与 CI/CD；

参与 GPU/NPU 资源调度与集群利用率优化，提升推理服务稳定性与云平台资源管理效率；

跟踪开源社区（如 vLLM、TensorRT-LLM、SGLang、LMDeploy 等）前沿动态，完成技术调研、验证与落地；

与算法、业务和平台团队协作，优化端到端推理体验。

硕士及以上学历，计算机、AI、数学、信息、电子等相关专业；

熟悉 Python，了解大模型 Transformer 基本工作原理；

具备良好的工程习惯，熟悉 Linux、Git，学习能力强，能阅读英文技术资料；

了解或对 Kubernetes/K8s、Docker、云平台、云原生技术有浓厚兴趣；

乐于沟通协作，对 AI Infra、大模型推理方向有热情。

加分项（非必需） 有 vLLM、SGLang、TensorRT-LLM、LMDeploy 等推理框架使用或二次开发经验；

有 CUDA/Triton 算子编写经历；

有 Kubernetes、Docker、云平台、Ray、Volcano、Kueue、Prometheus/Grafana 等云原生或 MLOps/LLMOps 实践经验；

有 LLM 相关论文、开源项目、竞赛经历或高质量技术博客。

我们提供 资深大佬 1v1 带教，直接参与核心模块，拒绝边缘打杂；

真实业务场景 + 充足 GPU/NPU 资源，以及 K8s/云平台真实生产环境；

一群热爱技术的同龄人，一起把技术做深做透；

接触大模型推理与云原生 AI 基础设施前沿，支持技术分享、开源贡献与论文发表；

有竞争力的校招薪酬与福利保障。

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