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Open nowPosted 88 days ago

AI Infra 研究员

wcn7iq2vq1am/86371186 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowAI Infra 研究员wcn7iq2vq1am/863711 · Shanghai, Chinese Mainland

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  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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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. wcn7iq2vq1am/863711 postings stay open a median of 1 days.

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 88 days ago

wcn7iq2vq1am/863711 median: 1 days open

The posting

关于职位： 您将致力于探索扩散模型在端侧及单节点环境下的推理加速前沿技术，通过研究全新的算法原理与性能边界，为具身智能机器人系统提供极致的实时响应能力。本岗位侧重于从底层原理出发进行原型验证与算法创新，旨在突破复杂模型在受限算力下的性能瓶颈；研究工作本身相对独立，但因应用导向需与工程及硬件团队保持交流协作。

核心职责： — 开展面向多模态扩散模型（Diffusion Model）的高效采样、蒸馏及快速去噪算法研究。 — 探索模型压缩与高效推理的前沿技术，包括但不限于稀疏化、量化及面向端侧的缓存机制研究。 — 利用 Triton/CUDA 等工具对底层内核进行性能建模与分析，验证新型加速算法的可行性。 — 产出高质量的算法原型与 PoC，并与工程及硬件团队协同，推动研究成果向应用转化。

  • 计算机科学、人工智能或相关领域学士/硕士/博士学位（博士学位优先）。
  • 深入理解扩散模型原理，并具备模型压缩与高效推理方向的研究背景。
  • 熟悉多模态模型的前沿研究进展，并具备通过 Triton/CUDA 进行底层性能研究或建模的能力。

优先条件： — 在相关领域顶会有学术论文发表记录者优先。 — 具有端侧设备或单节点环境下生成式模型加速研究的实践经验者优先。

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