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Open nowPosted 93 days ago

具身大模型Infra Engineer

xiaopeng/index1,577 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now具身大模型Infra Engineerxiaopeng/index · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 93 days ago

The posting

1. 大规模训练系统与 Scaling：面向可泛化、可持续 Scaling 的具身大模型（VLA Foundation Model），负责大规模分布式训练框架的设计、优化与迭代，支撑随数据量持续增长的训练需求，是基模能力持续突破的核心环节。 2. 训练性能优化与工程化落地：主导多模态大模型的训练性能优化——包括并行策略（数据/张量/流水/序列并行）、显存与通信优化、混合精度、算子与 IO 优化等，持续提升训练吞吐与资源利用率，缩短模型迭代周期。 3. 数据—训练链路建设：与数据、算法团队协作，打通「海量多模态数据→ 高效训练 → 快速验证」的闭环，保障数据规模增长下训练链路的稳定性与可扩展性。 4. 技术影响力：紧跟大模型训练系统与 infra 领域的国际前沿进展，通过关键技术攻坚沉淀方法论与基础设施，形成持续的技术影响力。

1. 教育背景：计算机、电子工程、人工智能、自动化等相关专业硕士及以上学历； 2. 算法与系统基础：具备扎实的机器学习/深度学习基础，深入理解大模型（VLM / LLM / VLA 等）的训练原理与工程实践； 3. 编程与工具能力：精通 Python / C++，熟悉 Linux 环境与网络/性能分析；熟练使用 PyTorch，深入理解 CUDA、NCCL 及底层计算/通信机制，具备优秀的系统级代码实现能力； 4. 核心专业技能：具有丰富的大规模训练平台经验，熟悉主流分布式训练框架（如 Megatron-LM / DeepSpeed / FSDP / Ray 等），有千卡级别及以上大模型训练的性能剖析与优化实战经验；有多模态/具身大模型训练经验者优先； 5. 综合素质：对具身智能（Embodied AI）及大规模模型 Scaling 有浓厚兴趣；具备良好的跨团队沟通协作能力与复杂系统问题的解决能力，能在"数据—算法—训练平台"多方之间高效推进项目。

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