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Open nowPosted 165 days ago

AI Infra 工程师

agirobot/referral515 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowAI Infra 工程师agirobot/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 agirobot/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. agirobot/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 165 days ago

agirobot/referral median: 1 days open

The posting

训练方向： 1. 大规模分布式训练系统：在千卡级 GPU 集群上构建稳定、高效的分布式训练系统，支持 VLA 等具身大模型的预训练与微调； 2. 训练效率优化：优化大规模训练中的计算效率、通信效率与 I/O 效率，消除训练瓶颈，提升集群整体利用率； 3. 训练数据加载流水线：构建从存储到 GPU 显存的高吞吐数据流水线，实现高效的数据预取、采样与加载，消除训练过程中的 I/O Stall； 4. 训练稳定性保障：解决大规模训练中的故障恢复、Checkpoint 管理、梯度异常等问题，确保长时间训练任务的稳定运行。

推理方向： 具身模型推理与优化开发 设计面向具身机器人本体亲和的轻量化、高性能推理框架，支持CNN、Transformer、Mamba、GNN等主流网络结构； 面向GPU/NPU的具身多模态算子开发与性能优化，实现内存复用、算子融合、数据排布优化、多流并行等通用加速策略; 开发模型解析、转换、性能剖析、可视化profiling工具； 具身模型性能分析与系统部署 对模型推理过程进行时间、功耗、内存占用分析，定位瓶颈并设计软硬协同优化策略； 实现支持 PTQ/QAT 的量化工具链(INT8/INT4/FP8/MXFP8等浮点混合精度)； 探索结构化剪枝、算子重参数化、神经架构搜索与推理引擎的联合优化。 前沿探索 跟进VLA、世界模型、空间智能等最新算法架构趋势，及时洞察捕捉低精度量化、AI KernelGen、投机推理等在具身本体上的模型高效推理适配技术;

训练方向： 1. 千卡级训练实战经验：有千卡级 GPU 集群上的大模型训练实战经验，熟悉大规模训练中的常见问题与解决方案； 2. 分布式训练框架：精通 PyTorch 分布式训练机制（DDP/FSDP），熟悉 DeepSpeed、Megatron-LM 等大规模训练框架的原理与使用； 3. 并行策略：深入理解 多维并行（Data Parallel / Tensor Parallel / Pipeline Parallel）的实现原理与适用场景，能够根据模型特点设计最优并行策略； 4. 性能分析与调优：熟练使用 PyTorch Profiler、NVIDIA Nsight 等工具进行性能分析，能够定位并解决计算、通信、I/O 瓶颈。

推理方向： 专业技能 熟练掌握C/C++/Python/Go至少一种编程语言，具备CUDA/OpenCL/CANN优化经验； 深入理解至少一种推理引擎架构(如tensorrt/vllm/sglang/ktransformer/llama-cpp)推理框架架构； 熟悉模型量化原理(KL 散度、MinMax、LSQ、AWQ、GPTQ等)及相关工具链； 熟悉常见典型算子实现与优化(卷积、矩阵乘、激活、LayerNorm、Softmax、flashattention等)； 具备AI处理器profiling能力(perf、简单性能计数器、硬件事件、功耗测量)与AI计算负载量化分析能力； 加分项 参与过开源推理引擎贡献(MNN、Tengine、OpenCV、TVM、vLLM、SGLang等)； 熟悉MLIR、IREE、Triton、TileLang等编译栈或Dialect开发；

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