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Open nowPosted 135 days ago

多模态大模型【推理】优化工程师（AI Infra）

acnc6zeentra/school19 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now多模态大模型【推理】优化工程师（AI Infra）acnc6zeentra/school · Shanghai, Chinese Mainland

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Early applications get read.

12.1% 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. 4.5%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 135 days ago

The posting

1. 负责公司内部大模型推理引擎的维护、开发与性能优化，面向音视频理解和生成等多模态推理场景进行深度适配 2. 面向线上业务构建高吞吐、低延迟、稳定可观测的大规模推理系统，满足严格的 TTFT、TPOT、E2E latency、goodput 和 p99 SLO 要求 3. 跟踪 vLLM/vLLM-Omni、SGLang/SGLang-Omni、TensorRT-LLM 等推理引擎的演进，并结合生产场景进行深度优化和工程落地

1. 具备扎实的计算机系统、体系结构和并行计算基础；精通 Python，熟悉 C++，熟练使用 PyTorch 2. 熟悉大规模分布式推理系统，理解 PD 分离、DP attention 等部署策略 3. 掌握 vLLM/vLLM-Omni、SGLang/SGLang-Omni、TensorRT-LLM 等推理引擎的核心设计，理解 scheduler、attention backend、KV cache manager、prefix cache、batching 和 serving runtime 4. 有千亿参数级以上模型线上部署经验，能够定位和解决 queue wait、decode bottleneck、cache miss、显存水位、长尾请求、OOM、通信抖动和多租户干扰等问题 5. 具备生产环境性能分析和稳定性治理能力，能够基于 metrics、trace、profile、日志和压测结果持续优化线上 SLO

加分项 1. 作为核心成员参与过业界知名模型的推理优化或大规模线上部署 2. 在 vLLM/vLLM-Omni、SGLang/SGLang-Omni、TensorRT-LLM 等开源推理项目有实际贡献 3. 熟悉 CUDA、Triton kernel、CUTLASS kernel 优化，对 attention、sampling、quantization、MoE routing、multi-modal projector 等热点模块有优化经验 4. 以第一作者在 MLSys、OSDI、SOSP、NSDI、ASPLOS 等系统或体系结构会议发表过论文 5. 长期跟踪推理系统、模型结构和部署架构演进，对多模态模型的线上成本、性能和稳定性有工程化判断

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