LLM 推理算法工程师
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推理架构演进： 参与或主导分布式大模型推理系统的架构演进，设计并落地 Prefill / Decode 分离架构（P/D Separation）、全局 KV 缓存路由（KV Router）等核心基础设施功能。 底层算子与量化优化： 负责低比特量化推理方案（如 FP8、W4A8 等）的生产级落地；深入优化 Attention 与 MoE 等核心算子，提升单卡吞吐量，降低单 Token 推理成本。 复杂场景专项优化： 针对会议总结等超长文本场景，深度重构和优化 Chunked Prefill 机制与显存置换（Swapping）策略，解决极限并发下的显存碎片与网络传输开销；优化多轮对话下的 Prefix Caching 及投机采样（Speculative Decoding）实现，降低系统首字延迟（TTFT）。 性能剖析与调优： 建立完善的推理性能可观测体系（Profiling），从系统级、算子级、通信级深入剖析软硬件性能瓶颈，指导系统架构与调度算法的迭代。
岗位要求
计算机及相关专业本科或以上学历，具备扎实的计算机体系结构及操作系统基础。 精通 C/C++ 与 Python，具备扎实的 CUDA 编程能力，熟悉 Triton/Cutlass，有实际的算子深度调优及业务落地经验。 深入理解主流开源推理框架（如 vLLM, TensorRT-LLM, SGLang），具备源码级的二次开发、定制优化及复杂 Bug 排查能力。 熟悉 Transformer 及主流大语言模型架构，了解模型量化、剪枝、稀疏化等主流压缩加速技术。 了解底层分布式通信协议（NCCL/RDMA），对多机多卡环境下的并行计算策略（TP/PP/EP）有实践经验。
加分项
在大模型推理系统（vLLM, SGLang, TRT-LLM等）或高性能计算开源项目中作为核心贡献者。 对 NVIDIA Hopper/Blackwell 等最新微架构特性有深入理解与实战经验
Ways of Working
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