[Skip to main content](#main-content)

[OpenNash Jobs](https://jobs.opennash.com/)
[Hiring report](/reports/hiring/) · [All Meituan jobs](https://jobs.opennash.com/companies/meituan/) · [Search jobs](/search/)

Meituan · 北京市 +1 more

# 【大模型北斗实习】图像生成大模型技术研究

北京市 +1 more 

Listed on Meituan ’s official careers site. Listed as of October 19, 2026. Check that it is still open and apply there.
[Apply on Meituan ’s careers site →](https://zhaopin.meituan.com/web/position/detail?highlightType=campus&jobUnionId=3155424457)

[OpenNash Jobs](https://jobs.opennash.com/) / [Meituan ](https://jobs.opennash.com/companies/meituan/) / 【大模型北斗实习】图像生成大模型技术研究

## Job description

在AIGC大模型的RLHF阶段，奖励模型（Reward Models）通过提供精准的奖励反馈来调整模型优化方向，以提升图像生成的质感与美感，从而增强整体视觉呈现效果。同时，随着模型参数规模的不断增加，亟需从模型架构层面探讨更优的生成策略。

### 课题研究内容包括但不限于

1）多维度图像奖励模型的设计。 2）DiT框架的优化，探索性能与推理效率的极限。 3）图像生成范式的探索，系统性验证AR或AR+Diffusion方案的优缺点，以迈向更大规模的生成模型。 1）熟悉扩散模型研究进展。 2）熟悉PyTorch，能快速开展实验。

### 加分项

1）有图像/视频生成大模型研究经验，或者在相关开源项目中做出过核心贡献。 2）对AIGC的训练与推理优化有实践经验。 3）在计算机视觉、多媒体、人工智能的权威期刊或会议上发表过视觉生成相关的论文专著，或者在重要算法比赛中取得过优秀名次。

## 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: 北京市 +1 more[Apply on Meituan ’s careers site →](https://zhaopin.meituan.com/web/position/detail?highlightType=campus&jobUnionId=3155424457)

## More jobs at Meituan 

### [AI Agent 产品经理](https://jobs.opennash.com/companies/meituan/jobs/2b6311eacacf7247913a/)

北京市 +1 more

### [AI Agent 研发工程师](https://jobs.opennash.com/companies/meituan/jobs/f9b3d4aa95acf5819eeb/)

北京市 +1 more

### [AI Agent工程师](https://jobs.opennash.com/companies/meituan/jobs/5f93a9700c1ee721af06/)

北京市 +1 more

### [AI Builder（产研范式方向）](https://jobs.opennash.com/companies/meituan/jobs/b24feb70be908b5d1104/)

北京市 +1 more

### [AI Builder（研发工作流方向）](https://jobs.opennash.com/companies/meituan/jobs/5f783bdfef00cdea64c2/)

北京市 +1 more

OpenNash lists jobs from employers’ own careers sites and does not handle applications.
Dates come from the employer where stated; “first seen” and “last checked” are our own observations.

OpenNash Jobs · Official listings with dates and links to the employer. [Weekly hiring report](/reports/hiring/) · [Methodology](/methodology/)