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Open nowPosted 67 days ago

VLA大模型部署高级工程师

xiaopeng/index1,577 open roles

Where
Guangzhou, Guangdong, Chinese Mainland
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Your applicationOpen nowVLA大模型部署高级工程师xiaopeng/index · Guangzhou, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.9% 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. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 67 days ago

The posting

【团队介绍】 以通用大模型技术为核心引擎，驱动从感知到决策的端到端一段式智能架构。我们深耕多模态融合、世界模型与闭环强化学习等前沿技术，将算法创新持续落地于不断迭代的小鹏智能驾驶汽车、Robotaxi、新型出行工具及具身机器人等核心产品，推动智驾与具身智能从实验室走向真实世界。部署团队负责打通VLA大模型从训练产出到车端/机器人端实际运行的最后一公里，是算法能否真正落地的关键环节。

【加入我们，你将获得】 直面真实部署难题：面对车端/机器人端严苛的算力、时延、功耗约束，独立负责VLA大模型的量产级部署方案； 全链路视野：打通"模型结构设计-训练-压缩-算子优化-推理框架-芯片部署"全流程，不做单点螺丝钉； 快速成长空间：部署效果直接影响量产体验，优秀者可主导某类芯片/某条推理链路的核心优化工作。

【职位描述】 1、负责VLA大模型（多模态感知、一段式端到端模型、世界模型等）在车端/机器人端的部署落地，完成模型压缩、量化、算子融合等关键优化，满足实时性与资源约束； 2、针对目标推理框架进行适配开发与性能调优，解决大模型部署中的精度损失、显存/内存瓶颈、算子不支持等工程问题； 3、参与自定义算子开发与性能优化，结合具体芯片平台特性进行算子级别的调优，提升推理吞吐与降低时延； 4、设计并实现模型量化方案（PTQ/QAT等），在精度与效率之间做出合理权衡，推动量化方案在实际业务场景中的落地验证； 5、与算法团队协同，从部署视角反哺模型结构设计与训练策略（如量化友好结构、稀疏化、蒸馏等），提升模型的可部署性； 6、持续跟踪业界推理加速、模型压缩相关的前沿技术，评估其在VLA大模型部署场景下的适用性并推动落地。

1、硕士及以上学历，计算机科学、电子工程、自动化、人工智能等相关专业，毕业2年以内； 2、对大模型部署有扎实的实战经验：熟悉至少一种主流推理框架（TensorRT/ONNX Runtime/TVM/vLLM等），有实际项目中完成模型部署上线的经验； 3、熟悉模型算子层面的优化：了解常见算子（Attention、Conv、MatMul等）的计算特性，有算子开发或算子级性能调优经验者优先； 4、精通量化技术：熟悉PTQ/QAT等主流量化方法，理解量化对模型精度的影响机制，有实际量化方案落地经验； 5、对模型结构和训练有一定理解，熟悉Transformer、扩散模型等主流架构，了解SFT/RL等后训练方法，能够从部署反向推动模型设计的合理性； 6、熟练掌握Python，熟悉C++/CUDA者优先；具备良好的工程能力和性能调试能力（profiling、瓶颈定位等）； 7、具备较强的问题拆解与解决能力，能独立定位部署过程中的性能瓶颈并提出优化方案； 8、加分项：有大模型（LLM/VLA/多模态）量产部署经验、异构芯片（NPU/车规芯片）部署经验、开源推理框架贡献经验，或相关论文发表。

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