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Open nowPosted 93 days ago

Embodied AI System Engineer

xiaopeng/index1,586 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen nowEmbodied AI System Engineerxiaopeng/index · Shenzhen, Guangdong, 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. xiaopeng/index postings stay open a median of 1 days.

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 93 days ago

xiaopeng/index median: 1 days open

The posting

1. 模型量化与压缩（核心）：面向具身大模型（VLA），主导 PTQ（训练后量化）与 QAT（量化感知训练）等量化方案的设计与落地，在低比特/混合精度下完成模型压缩，兼顾模型效果与运行效率。 2. 精度与性能对齐：建立量化前后的精度评估与回归验证机制，解决量化带来的精度损失问题，保障压缩后模型在真实任务上的效果稳定、可交付。 3. 推动模型高效落地：作为模型侧的核心推动者，与硬件、工程及测试团队紧密协作，输出量化模型与优化方案，驱动大模型与机器人感知、决策、控制模块的系统级集成与落地，打通模型能力落到真实本体的 last mile。 4. 技术影响力：紧跟模型压缩、量化与推理优化领域的最新进展，通过技术攻坚与方案沉淀，持续提升模型在资源受限场景下的运行效率，形成技术影响力。

1. 教育背景：计算机、电子工程、人工智能、自动化等相关专业本科及以上学历； 2. 算法与工程基础：具有扎实的机器学习/深度学习基础，深入理解 VLM / LLM / VLA 等大模型的结构与推理过程； 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 等主流深度学习框架，具备优秀的系统级代码实现能力； 4. 核心专业技能：精通模型量化技术，深入掌握 PTQ 与 QAT 的原理与工程实践，有大模型/多模态模型量化压缩并完成精度对齐的实战经验；熟悉主流量化工具链与推理加速方案（如 TensorRT / ONNX / TVM / CUDA 等），了解算子优化与异构计算者优先； 5. 综合素质：对具身智能（Embodied AI）及机器人系统有浓厚兴趣；具备良好的跨团队沟通协作能力与复杂问题解决能力，能作为模型侧核心，在"研究—工程—硬件"多方之间高效推动项目落地。

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