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Open nowPosted 156 days ago

具身智能大模型训练系统开发与优化实习生

agirobot/internrecruitment362 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now具身智能大模型训练系统开发与优化实习生agirobot/internrecruitment · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% 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. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 156 days ago

The posting

将会参与如下四个典型训练系统优化方向工作(包括但不限于下面四个方向) 参与方向一：支撑大规模预训练/微调的高效稳定运行 关键任务： 1. 参与千卡级别分布式训练集群的框架优化，确保训练任务在大规模集群上的稳定性（任务失败率 < xxx%）和可恢复性（断点续训时间 < xxx分钟） 2. 优化训练任务的吞吐量（Throughput），相比基线提升至少 20% 3. 参与至少一种并行策略（数据并行、模型并行、流水线并行、MoE并行）的框架级实现或深度优化

方向二：降低大模型训练显存占用，支持更大规模模型 关键任务： 1. 集成或优化至少一种显存节省技术（ZeRO-1/2/3、重计算（Activation Checkpointing）、混合精度训练），使相同GPU下的可训练参数量提升 30% 以上 2. 参与FlashAttention、Flash-FFN等高性能算子在分布式训练框架中的集成与适配 3. 验证并对比不同并行策略组合（如 FSDP + 张量并行）的显存效率与计算效率

方向三：优化跨节点通信效率，降低通信开销 关键任务： 1. 使用 NCCL 或 CANN ACL profiling工具分析通信瓶颈（AllReduce、AllGather等），并提出至少 2 项有效优化方案 2. 参与实现通信与计算的重叠（overlap）优化，使通信隐藏比例提升至 50% 以上 3. 探索并验证低比特通信（如 FP8 梯度通信）在训练中的可行性与效果

方向四：完善训练框架的可用性与可观测性 关键任务： 1. 开发或优化训练监控 Dashboard，覆盖多类关键指标（GPU利用率、内存占用、通信耗时、吞吐量、损失曲线） 2. 参与实现训练任务的自动容错与恢复机制，支持节点级/进程级故障自动重调度 3. 撰写至少内部技术文档/最佳实践指南，帮助算法团队更高效使用训练框架

1. 熟悉计算机体系结构，理解CPU/GPU/NPU、内存层级、PCIe/NVLink等硬件互联架构； 2. 熟练掌握 C/C++ 和 Python，有良好的工程能力； 3. 熟悉 PyTorch 分布式训练生态（DDP、FSDP、DTensor、Torch.compile等）； 4. 了解大模型典型架构（Transformer、Attention、MoE、MHA/GQA/MQA等）；

分布式系统方向（满足一项或多项） 1. 有分布式训练实际经验，熟悉NCCL、MPI、GLOO等通信库； 2. 了解或使用过Megatron-LM、DeepSpeed、FairScale、MindSpeed等大规模训练框架； 3. 熟悉集合通信原语（AllReduce、AllGather、ReduceScatter、All2All等），理解其在模型并行中的应用； 4. 有集群调度系统经验（Slurm、Kubernetes、Ray）者优先；

性能优化方向（加分项） 1. 有CUDA或CANN开发经验，熟悉GPU/NPU编程模型； 2. 熟悉训练性能profiling工具（Nsys、Nsight Compute、PyTorch Profiler、Tracing）； 3. 了解FlashAttention、AllGatherMatMul、MatMulReduceScatter、MatMulAllReduce等高性能算子实现原理；

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