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Open nowPosted 260 days ago

视频生成算法工程师

acnc6zeentra/index41 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now视频生成算法工程师acnc6zeentra/index · Shanghai, Chinese Mainland
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  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 260 days ago

The posting

你会做什么 1. 参与音视频生成模型研发,覆盖图像到视频、文本到视频、视频到音频、音视频联合生成、参考生成及视频编辑等方向,探索模型如何同时学习画面质量、运动结构、镜头语言、声音事件、高表现实力语音和跨模态一致性。 2. 参与大规模预训练体系建设,包括数据管线、训练策略、评测体系、数据配比以及 Scaling 实验的设计、执行和分析。 3. 研究和训练更好的表示空间,探索如何同步在表示空间的可学习性、重建质量和压缩率之间找到权衡。 4. 参与生成模型结构设计与探索,扩展视频生成长度、提升模型的可控性以及生成效率。 5. 参与模型后训练,包括高质量SFT,设计贴近用户真实使用场景的新模型特性和能力点及对应的数据管线、训练策略和评测方法。探索视频生成模型的自动化评测方法。探索视频模型的奖励函数建模以及强化学习等前沿路线落地的可能; 6. 探索 AI-First 的工作模式,使用 AI-for-AI 的方式加速基础模型的训练,共同创建 AI-Native 的高效协作方式。 7. 跟进视频生成、音频生成、多模态生成、世界模型、后训练、模型压缩和推理系统方向的前沿工作,并把真正有效的方法沉淀到内部流程和模型能力中。

我们希望你 1. 相信建模物理世界复杂情境的能力是 AGI 的必不可少的部分,对音视频生成基础模型有强烈兴趣并愿意作为自己的事业,认为这是一个涉及表示、数据、系统、规模化和审美判断的科学问题。 2. 至少在一个方向上有比较深度的积累或探索,体现在开源贡献、技术博客、论文等形式上:扩散模型 / flow matching / 自回归等生成建模方法、视频或音频的 VAE 与 tokenizer、DiT 类多模态架构与跨模态对齐、视频/音频理解及其对生成的反哺、面向生成模型的 RLHF 与偏好对齐、大规模音视频数据工程、扩散模型步数蒸馏及量化、分布式训练与推理优化(含 CUDA / Triton)等。 3. 有扎实的机器学习或工程基础,熟悉 PyTorch ,理解深度学习训练的基本流程,愿意深入到 loss、数据、显存、吞吐、采样和评测细节里,有解决问题的能力。 4. 能在不确定性里推进问题。敏锐捕捉前沿问题(如 World Model, Embodied AI),能够独立思考。追求可规模化的改进,敢于在更大规模的数据(如互联网视频流)和参数量下思考问题。更多追求获得工业界影响力或开源社区贡献,而不是单纯堆砌论文数量。 5. 熟练使用 Codex、Claude Code 等前沿 AI Harness 工具,并能用它们提升研究、工程、数据处理和写作效率。 6. 沟通直接、诚实、谦逊,愿意和模型、数据、工程、评测、产品同学一起讨论问题来源,而不是只维护自己的局部方案。 加分项 1. 有大规模训练经验,熟悉多机多卡、FSDP / DeepSpeed / Megatron、混合精度、profiling、训练稳定性、数据吞吐等监控和管理。 2. 熟悉后训练方法及相关流程,包括 preference modeling、reward modeling、RL、rejection sampling、model-as-judge、human eval、Arena 或 GSB Test 等。 3. 对影视语言、摄影、剪辑、动画、声音设计、音乐、游戏、3D 或创作工具有长期兴趣。

你会收获什么 1. 完整经历音视频生成基础模型从表示、预训练、后训练到推理系统的端到端研发,理解各环节如何相互咬合,而不是只接触某个孤立模块。 2. 在足够大的数据和算力规模下做实验和决策,沉淀关于"什么是可规模化的改进"的判断力,理解哪些想法在 scale up 之后仍然成立。 3. 和 Claude、Codex 等前沿 AI 工具深度共事,亲身参与 AI-First 研发体系的塑造,把研究、工程、数据、写作的杠杆整体放大。 4. 把你在生成建模、表示学习、后训练或训练系统上的想法,做成对工业界和开源社区有真实影响力的产出,而不是停留在论文数量或榜单指标上。 5. 和一群对问题负责、对证据诚实、愿意被反驳的同事长期共事,习惯把每一个判断都放回到数据和实验里验证。 这份工作适合你,如果你愿意 1. 把较长一段时间投入到音视频生成基础模型这一个方向上,而不是追逐每一个短期热点。 2. 在还没有标准答案的问题上工作——接受很多决定要在信息不足时做出、很多实验会失败、很多结论会被新的证据推翻。 3. 把模糊的直觉转化为具体、可验证的实验、数据与代码,而不是停留在抽象讨论或局部 demo。 4. 主动调整研发习惯去适配 AI-First 工作流,把 Codex、Claude Code 等工具用透,并把可以被 AI 加速的部分主动让出。 5. 跨过模型、数据、系统、评测和产品的边界看问题,而不是只守住自己最熟悉的那一段。 6. 直接、诚实地表达不同意见,也接受别人同样直接地反驳你;把"对问题负责"放在"对自己方案负责"之前。 欢迎来 MOSI。

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