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Open nowPosted 109 days ago

训练推理优化工程师(Mstar)

momenta/campus30 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now训练推理优化工程师(Mstar)momenta/campus · Beijing, Chinese Mainland

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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.

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 109 days ago

The posting

负责Momenta自动驾驶大模型、世界模型的分布式训练推理框架研发，主要包括： 1、参与设计、研发、维护团队内部的模型分布式训练框架，擅长分析并深度优化训练各个阶段的性能瓶颈，包括计算效率、通信延迟、显存占用等； 2、结合不断迭代的模型算法逻辑，设计并实现针对性的高效分布式并行训练策略； 3、深入研究 CUDA、NCCL、RDMA 等编程范式和通信库，针对团队内部的GPU和集群拓扑约束，开发高性能算子 并 优化分布式通信效率，达到行业SOTA水平； 4、深入研究低精度混合精度训练策略，在保证模型精度满足预期的情况下，探索低精度（FP8、FP4）训练的性能极限； 5、配合算法需求，开发RL训练框架、迭代RL训练算法逻辑，优化在线Rollout推理性能，深度优化分布式On-Policy/异步RL训练效率。

学历背景： 计算机相关专业硕士及以上学历，具备扎实的理论基础和强大的动手能力； 编程能力： - 熟悉 Python/C++/CUDA/CUTLASS/Triton编程，熟悉PyTorch框架及其底层实现； - 熟悉分布式系统开发与调试，熟悉分布式通信开发NCCL/RDMA/IB/RoCE，有多进程调度与并行算法优化经验者优先； 训练与推理经验： - 熟悉大模型结构与算法（LLM/VLM/VLA/DiT、MoE架构、各种RL算法等）； - 熟悉分布式训练/推理的常用策略（DP/TP/PP/EP/CP、recompute、offloading、PD分离等）； - 有主流开源分布式训练框架（如Megatron-LM、DeepSpeed、VeRL）或推理框架（如SGLang、vLLM）的深度二次开发与优化经验。

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