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Kuaishou Technology · Beijing, Shanghai

# （可灵AI专项）大模型算法加速/优化实习生

Beijing, Shanghai 

Listed on Kuaishou Technology’s official careers site. Listed as of October 19, 2026. Check that it is still open and apply there.
[Apply on Kuaishou Technology’s careers site →](https://zhaopin.kuaishou.cn/#/official/social/job-info/27889)

[OpenNash Jobs](https://jobs.opennash.com/) / [Kuaishou Technology](https://jobs.opennash.com/companies/kuaishou-technology/) / （可灵AI专项）大模型算法加速/优化实习生

## Job description

1、diffusion步数蒸馏和CFG蒸馏： 1）跟进学术界最前沿的 few-steps diffusion 算法, 探索研发 one-step / few-steps 的无损蒸馏算法, 包括但不限于 consistency modeling、shortcut、score distillation等方案, 打造实时化的极致高效视频/图像大模型； 2）研究 DPO/GRPO/PPO 等强化学习的手段在少步模型下的新训练算法, 摸索强化学习与压缩算法的化学反应, 推动效果-效率的帕雷托前沿新SOTA； 2、transformer模型架构优化： 1）跟进学术界高效attn 计算成果(包括但不限于: sparse-attn, linear-attn 等), 探索研究视频模态下较full-attn 的无损高效attn方案； 2）量化后训练: 探索极低bit 的量化训练(QAT), 特别是针对FP8/FP4 的无损量化加速。
- 硕士及以上学历，数学、计算机、自动化等专业优先；
- 熟练掌握diffusion原理, 具备公式推理和证明的能力, 有过相关的研究经历和实践经验者优化；
- 熟悉transformer结构及其变种, 熟悉attention的设计和多种变种实现, 掌握triton；

### 数理要求
- 熟练掌握线性线数、概率论、信息论、凸优化等基础知识，了解矩阵论、随机过程等；
- 有较强的自驱力和学习力，对生成式模型有强烈的兴趣。

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 19, 2026
- Last checked: Oct 19, 2026
- Listed for: Under a day
- Location: Beijing, Shanghai[Apply on Kuaishou Technology’s careers site →](https://zhaopin.kuaishou.cn/#/official/social/job-info/27889)

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