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Open nowPosted 133 days ago

视觉多模态大模型算法工程师

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工程师acnc6zeentra/school · Shanghai, Chinese Mainland

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  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 133 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。设计融合图像、视频与文本的统一建模方案，攻克长视频的时序因果推理、情节记忆与关键信息召回，强化对微小动作、物体轨迹与交互关系的时空细粒度感知，实现复杂动态场景下的精准理解与推理。 2. 核心能力构建：围绕视觉感知、空间与时序理解、OCR、视觉推理等核心能力，设计高质量训练数据的构建与合成方案及配套训练策略，持续突破模型的能力上限。 3. 训练策略与数据配比：参与训练策略设计，包括预训练 / 后训练阶段划分、课程学习、loss 与超参策略；通过系统的消融实验研究不同来源、类型与难度数据的最优配比，从全局定位并补齐模型短板。 4. 后训练与对齐：探索强化学习等后训练方法，设计奖励与偏好数据，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：探索流式、因果、可在线感知与主动交互的视频理解能力——这是我们区别于多数「离线」视频理解工作的特色方向。

1. 扎实的工程功底：计算机、人工智能或数学等相关专业应届毕业生（本科 / 硕士 / 博士均可）；数据结构与算法基础扎实，精通 Python，熟练掌握 PyTorch，具备良好的工程实践与代码风格；了解或使用过 DeepSpeed / Megatron 等分布式训练框架者优先。 2. 视觉 / 多模态认知：理解计算机视觉与多模态大模型的基本原理，熟悉主流 VLM 架构（ViT 视觉编码 + LLM），对多模态预训练与后训练范式（CLIP / LLaVA / Qwen-VL 一脉，及 SFT / RLHF / RLVR）有基本认知，并在视觉或多模态方向有过实际的动手经历（科研、实习或高质量复现均可）。 3. 实验方法论：对实验设计有基本的方法论意识，能通过对照与消融实验验证自己的假设，不满足于单点指标；对「数据 / 策略如何转化为模型能力」这条因果链有好奇心。 4. 学习能力与闭环意识：有完整推进过一个课题或项目的经历（科研 / 实习 / 竞赛 / 开源均可），能独立查文献、定位问题、动手验证、形成结论；具备快速学习能力与把事情做完的执行力。

加分项 1. 竞赛获奖：ACM/ICPC、NOI/IOI 获奖者，或在 Kaggle、CVPR / ICCV / ECCV / ACL Challenge 等顶级竞赛中名列前茅。 2. 学术成果：以主要作者身份在 CVPR / ICCV / ECCV / NeurIPS / ICML / ICLR / ACL 等顶级会议发表过视觉 / 多模态相关工作；相比数量，我们更看重工作的品味与影响力。 3. 实战经验：有大模型 / 多模态方向的实习经历，参与过训练数据构建、模型训练或 RLHF / RLVR 后训练；或曾独立端到端训练过一个真实模型（高质量开源复现亦可）。 4. 前沿嗅觉：对流式 / 在线感知、视觉推理、Agentic、World Model 等前沿问题有自己的观察与判断；有高质量的开源项目或技术分享。

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，深度参与从数据构建、预训练到后训练对齐的完整流程，而非只负责边缘模块。 2. 一线研究员带教：与有基座模型实战经验的 mentor 并肩工作，快速建立「数据—训练—评测」的完整方法论。 3. 清晰的成长路径：我们同时在为多个方向寻找未来的负责人；表现出色的校招同学将有机会独立扛起一个方向，从 0 设计方案并推动到结论。

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