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Open nowPosted 290 days ago

视觉多模态大模型算法工程师

acnc6zeentra/index41 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/index · Shanghai, Chinese Mainland

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Share of postings closed within
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  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 290 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：负责训练策略设计，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；数据结构与算法基础扎实，精通 Python，熟练掌握 PyTorch；能基于 DeepSpeed / Megatron 等分布式训练框架独立跑通并管理大规模训练实验；具备良好的工程实践与代码风格。 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）与视频时空建模，对多模态预训练与后训练范式（CLIP / LLaVA / Qwen-VL 一脉，及 SFT / RLHF / RLVR）有清晰认知。 3. 数据与实验方法论：对训练数据的质量、多样性与配比有敏锐判断；具备严谨的实验方法论，能通过消融实验把「数据 / 策略 → 模型能力」的因果链跑清楚，而非只盯单点指标。 4. 闭环思考能力：拥有从 0 到 1 端到端构建视频 / 多模态项目的完整经历，能从系统层面权衡模型能力、训练成本与迭代效率。

加分项 1. 竞赛与代码：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. 数据与后训练实战：有大规模训练数据构建 / 合成，或 RLHF / RLVR 等强化学习后训练的落地经验；曾独立端到端训练并迭代过一个真实模型。 3. 方向负责人潜质：有独立定义并扛起一个方向的经历——从 0 设计 recipe 与数据策略，并推动到可复用的结论。我们同时在为多个方向招募负责人。 4. 学术品味与前沿嗅觉：能敏锐捕捉前沿问题（流式 / 在线感知、视觉推理、Agentic、World Model 等），追求可规模化的改进；相比论文数量，我们更看重工业界影响力与开源社区贡献。

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