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Open nowPosted 187 days ago

【技术保障】模型推理优化专家

poizon/index568 open roles

Where
Hangzhou, Zhejiang, Chinese Mainland
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Your applicationOpen now【技术保障】模型推理优化专家poizon/index · Hangzhou, Zhejiang, 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 poizon/index's own form.

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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. poizon/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 187 days ago

poizon/index median: 1 days open

The posting

推理性能优化 1. 对LLM在线推理全链路进行性能分析与瓶颈定位，针对性优化 TTFT、TPOT、吞吐等核心指标。 2. 深入调优推理引擎（vLLM/SGLang/TensorRT-LLM等），包括 KV Cache 管理、continuous batching、量化、推测解码等关键技术。 3. 结合业务 SLO 要求，设计推理部署方案（机型选型、并行策略、batch 策略等），在满足延迟约束的前提下最大化 GPU 利用率 平台工程与成本优化 1. 基于Kubernetes设计和落地 GPU 工作负载的调度策略，包括算力调度、虚拟化资源管理、拓扑感知、亲和性、HPA等。 2. 识别低利用率、低性价比算力选型等资源低效问题，推动资源整合与优化，降低单位推理成本。 3. 参与 AI 平台产品设计和研发，从工程角度出发不断优化推理服务管理模式和流程，提升用户体验和效率。 业务协同与降本增效 1. 与业务团队深度合作，理解其推理场景特征（模型大小、请求分布、延迟要求），提供定制化的优化方案，并推进落地 2. 推进推理性能与成本的可观测体系建设，在大语言模型推理场景践行FIinops理念。

1. 具备扎实的 LLM 推理优化实战经验，能独立分析并优化TTFT、TPOT、吞吐等核心指标，有可量化的优化案例优先。 2. 深入理解至少一种主流推理框架（vLLM/SGLang/TensorRT-LLM/Triton等）的原理与调优方法。 3. 熟悉 Kubernetes 核心机制：调度器、资源模型、DevicePlugin机制、节点亲和性、HPA/KEDA等，有基于 Kubernetes 的 GPU 工作负载研发和运维经验。 4. 具备系统级性能分析能力，能使用 nsys、nvtop、perf、eBPF 等工具定位 GPU / CPU / 网络等瓶颈问题。

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