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Open nowPosted 66 days ago

大模型Infra工程师 (VLM推理)

shengshu/index98 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now大模型Infra工程师 (VLM推理)shengshu/index · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. shengshu/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 66 days ago

shengshu/index median: 1 days open

The posting

负责 VLM（视觉-语言模型）大规模推理服务的基础设施设计与优化。以极致吞吐与极致成本为核心目标，从服务架构到底层 Kernel 进行全栈压榨，在既定算力预算下支撑尽可能大的业务流量。 1. 推理服务架构设计与优化：主导 VLM 在线推理服务的架构设计与持续演进，深度定制 SGLang 推理框架，针对 VLM 特点（长视觉序列、Prefill/Decode 异构计算）进行系统级适配与优化。持续提升服务吞吐上限，降低单 Token 推理成本。 2. 显存管理与 KV Cache 极致压榨：针对 VLM 多图/长视频场景下 KV Cache 巨大的问题，设计高效的显存管理方案。深入理解并优化 SGLang 的 RadixAttention（前缀缓存）机制，结合 PagedAttention 等分页管理策略，最大化 Batch Size 与缓存命中率，提升单卡吞吐容量。 3. 推理成本极致优化：围绕单卡吞吐量与推理成本两大核心指标，落地并优化量化方案（FP8/INT4/AWQ/GPTQ）、投机采样（Speculative Decoding）等降本增效手段。在保证生成质量的前提下，持续提升每 GPU 的服务吞吐，降低单位 Token 的算力成本。 4. 全链路性能 Profiling 与调优：建立系统级性能看护体系，从请求接入、多模态数据预处理、Prefill 计算到 Decode 自回归生成，进行全链路吞吐拆解与瓶颈定位。优化 CPU 预处理与 GPU 推理之间的数据管道，最大化 GPU 有效计算占比。

1. 推理框架深度掌握：精读过 SGLang 核心源码，对其调度器（Scheduler）、RadixAttention 缓存机制、CUDA Graph 加速、Overlap 调度等核心模块有系统性理解与实战优化经验。熟悉 vLLM 并有对比认知者更佳。 2. VLM 推理认知：深刻理解 VLM 推理与纯文本 LLM 推理的差异（长视觉序列带来的 Prefill 压力、多图并行处理、视频抽帧与特征压缩等），并有针对性的架构优化经验。 3. 降本增效工具箱：熟悉主流量化推理方案（FP8/INT4/AWQ/GPTQ）的原理与落地代价，有投机采样（Speculative Decoding）的实战经验，能够在质量与吞吐之间做出精准的工程权衡。 4. 系统级性能调优能力：具备端到端的吞吐拆解能力，能够精准定位 CPU 预处理、数据传输、GPU 计算等各环节的吞吐瓶颈，并有系统化的方法论驱动持续优化。 5. 加分项： - 有 VLM（含视频输入）大规模推理服务落地经验，并有显著的吞吐/成本优化成果 - 熟悉多模态输入的预处理/后处理优化（如图像 Resize/归一化与 GPU 之间的零拷贝流水线） - 有 Triton Inference Server / TensorRT-LLM 等其他推理框架的深度使用经验

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