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Open nowPosted 135 days ago

视觉多模态大模型算法实习生

acnc6zeentra/school19 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now视觉多模态大模型算法实习生acnc6zeentra/school · Shanghai, Chinese Mainland

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  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 135 days ago

The posting

1. 视觉多模态基座模型研发：参与构建具备原生视频感知与推理能力的下一代多模态基座模型，深度参与到其中某个具体方向的研发中。 2. 训练数据构建与合成：围绕视觉感知、空间与时序理解、OCR、视觉推理等核心能力，参与高质量训练数据的构建、合成与清洗，分析数据质量对模型能力的影响。 3. 训练与消融实验：参与预训练 / 后训练实验的执行与管理，通过系统的消融实验研究数据配比与训练策略，定位并补齐模型短板。 4. 后训练与对齐：参与强化学习等后训练方法的探索，以及奖励与偏好数据的设计，降低幻觉、提升模型推理与交互的准确性与鲁棒性。 5. 实时 / 流式视频理解：参与流式、因果、可在线感知与主动交互的视频理解探索——这是我们区别于多数「离线」视频理解工作的特色方向。

1. 基本条件：计算机、人工智能或数学等相关专业在读学生（硕士 / 博士在读优先，能力突出的本科生亦欢迎）；每周到岗 4 天及以上，连续实习 6 个月以上，可尽快到岗者优先。 2. 工程基础：数据结构与算法基础扎实，熟练使用 Python 与 PyTorch，写过能跑、能读、能复现的训练代码；具备良好的代码风格。 3. 机器学习基础：理解深度学习与计算机视觉的基本原理，了解主流 VLM 架构（ViT 视觉编码 + LLM）；对 CLIP / LLaVA / Qwen-VL 一脉的多模态范式有初步认知者优先。 4. 动手与学习能力：有过任意一段完整的动手经历（课程项目、科研、竞赛、开源复现均可），能独立查文献、定位问题、动手验证；遇到不懂的东西愿意也能够快速学会。

加分项 1. 竞赛获奖：ACM/ICPC、NOI/IOI 获奖者，或在 Kaggle、CVPR / ICCV / ECCV / ACL Challenge 等竞赛中取得名次。 2. 科研经历：在视觉 / 多模态 / NLP 方向有科研经历，发表过论文或有在投工作。 3. 实战经验：复现或训练过 VLM / LLM 相关模型，接触过大规模数据处理或分布式训练；有高质量的开源项目。 4. 前沿热情：持续关注流式 / 在线感知、视觉推理、Agentic、World Model 等前沿方向，读论文、跑代码是日常习惯。

你将获得 1. 真实的基座模型训练经历：充足的算力与数据资源，直接参与下一代视觉多模态基座模型的研发，做有署名、有产出的核心工作。 2. 一线研究员一对一带教：与有基座模型实战经验的 mentor 并肩工作，建立「数据—训练—评测」的完整方法论。 3. 论文与开源机会：表现出色的实习成果有机会沉淀为顶会论文或开源项目，mentor 会全程支持。 4. 留用通道：表现优秀者可获得转正 / 校招绿色通道，优先成长为正式团队成员。

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