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Open nowPosted 16 days ago

系统架构工程师（推理服务器）

li/referral788 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now系统架构工程师（推理服务器）li/referral · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on li/referral's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. li/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 16 days ago

li/referral median: 1 days open

The posting

  • 负责推理服务器的系统架构规划与设计，定义整机硬件、AI 加速芯片、CPU、内存、存储、网络、散热、电源及管理模块之间的架构方案与关键接口。
  • 面向大模型推理、视觉推理、多模态推理等典型负载，分析计算、访存、互连、网络和存储瓶颈，制定系统性能优化方案。
  • 与 AI 芯片团队协作，完成芯片能力到服务器系统的映射，包括 PCIe/CXL/高速互连、内存体系、I/O 架构、功耗与散热、可靠性等方案设计。
  • 与模型部署和推理软件团队协作，理解框架、算子、并行策略、KV Cache、批处理及资源调度等需求，推动软硬件协同优化。
  • 与操作系统团队协作，定义 OS、驱动、固件、BMC、资源管理和可观测性相关的系统需求，优化设备管理、性能调优和故障诊断能力。
  • 与硬件研发团队协作，输出服务器总体架构、模块规格、接口定义、关键器件选型原则及设计约束，并支持原理图、PCB、结构、散热和电源方案评审。
  • 牵头系统级性能、功耗、热设计、可靠性和可维护性评估，建立系统设计指标、验证方法及验收标准。
  • 跟踪服务器、AI 加速器、高速互连、内存与网络等技术演进，形成产品路线建议与技术储备。
  • 本科及以上学历，计算机、电子工程、通信、自动化、微电子或相关专业；具备 3 年及以上服务器、AI 硬件或高性能计算系统相关经验。
  • 具备服务器系统架构设计经验，熟悉 CPU、GPU/NPU/AI 加速器、内存、存储、网络、BMC、电源和散热等模块的系统集成与权衡。
  • 熟悉主流服务器接口与协议，如 PCIe、CXL、DDR、HBM、NVMe、Ethernet、RoCE/RDMA 等。
  • 理解 AI 推理工作负载特征，了解大语言模型或其他深度学习模型的推理流程、性能瓶颈及资源需求。
  • 具备较强的系统分析能力，能够从性能、功耗、成本、可靠性、交付周期和可维护性等维度进行架构决策。
  • 具备良好的跨团队协作、技术推动和文档输出能力，能够主导系统规格、接口文档、架构设计文档和评审材料的编写。
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One press. We do the rest.

  1. A CV for this posting

    Written against li/referral's own wording, from every piece of relevant proof in your profile.

  2. 25 readers review it

    Recruiter, hiring manager, skeptic and more read every draft, round after round. You get the best round.

    The review screen in CV Rocket: how each CV was read, round by round.

  3. We apply on li/referral's form

    Our application engine gets through the hardest forms there are. Where a question needs you, AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

    An application in CV Rocket: every answer filled in on the employer's form.

  4. Every reply, sorted

    li/referral's answer lands in your private mailbox, and we classify it on arrival: interview, question, rejection.

    The CV Rocket inbox: each employer reply classified as an interview, an action or a rejection.

  5. Reply with AI

    AI helps you write the email, checks it and sends it. We show you whether the recruiter read it.

  6. The interview in your calendar

    Full integration with your calendar. The invitation goes straight in.

    An interview invitation in the CV Rocket inbox, added to the candidate's calendar.
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Why it works

3×

more interviews than doing it yourself with ChatGPT.

ChatGPT writes a CV and never learns what happened to it. We see every reply. For each CV we know:

  • How it was written, and how the review scored it
  • When we applied, and how long after the posting went up
  • Which posting, which company, which city
  • Who got the interview, and who heard nothing

That is how we know which CVs get called.

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