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Open nowPosted 3 days agoWe saw it 100 min after it went up

训练框架实习生-多模态生成方向-2028 届

vrfi1sk8a0/379481106 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now训练框架实习生-多模态生成方向-2028 届vrfi1sk8a0/379481 · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

13.7% of postings close within 7 days. Measured by our own scanner across the market. vrfi1sk8a0/379481 postings stay open a median of 16 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.8%3 days
  3. 13.7%7 days
  4. 22.1%14 days
  5. 37.6%30 days
  6. 59.1%60 days
This job: posted 3 days ago

vrfi1sk8a0/379481 median: 16 days open

The posting

- 更长的视频、更高的分辨率，以及多模态生成模型的 RL 后训练，都需要更高效的训练系统。 - 你将参与 MiniMax 多模态生成训练框架研发，深入分布式训练、GPU 性能优化和 RL 训推协同，让模型实验跑得更快、更稳，支持算法团队验证新的生成与对齐方法。 你将参与的工作 1. 训练框架研发：面向视频、图像、音频等多模态生成模型，开发和优化预训练、SFT 流程，支持长序列、高分辨率和变长输入，完善 dataloader、Checkpoint 与故障恢复。 2. 分布式训练优化：参与并行策略、重计算、混合精度与通信计算 overlap 的实现，优化显存占用和训练吞吐，提升多机多卡训练效率。 3. RL 训练框架研发：和算法团队一起实现 rollout、reward 计算、策略更新与评测流程，支持采样策略和 reward 组件的灵活接入，开展多模态生成模型的 RL 后训练。 4. 训推协同优化：优化 rollout 与 training 之间的权重同步、样本传输和资源分配，探索异步 rollout，减少采样与训练等待，提升 RL 训练的 GPU 利用率。 5. 性能分析与验证：通过 Profiling 定位 Attention 等关键 kernel、通信和 I/O 瓶颈，开展算子融合与执行优化，验证性能收益、数值正确性及训练效果。

我们期待你 1. 计算机及相关专业在校生，算法与编程基础扎实，熟练使用 Python 或 C/C++。 2. 有 PyTorch 项目经验，理解 autograd、optimizer 和训练流程，愿意深入框架源码定位问题。 3. 了解 GPU 并行计算、内存访问或分布式计算，对训练加速和 RL Infra 有兴趣。 4. 熟悉 Linux，能阅读论文与源码，设计可复现的实验，验证性能收益和计算正确性。 加分项 1. 有多模态生成项目经验，了解 Diffusion、Flow Matching、DiT 等模型与方法。 2. 了解 PPO、GRPO 等 RL 方法，或参与过生成模型的 rollout、reward 接入、策略更新与评测。 3. 使用或优化过 FSDP、Megatron-Core、DeepSpeed 等分布式训练框架与工具。 4. 有 CUDA/Triton kernel 开发、通信优化或相关开源贡献。 5. 有 ICPC、NOI、ASC 等程序设计或超算竞赛经历。 你将收获 参与多模态生成模型从预训练、SFT 到 RL 后训练的系统研发，理解模型、训练算法和硬件效率之间的关系，通过框架和训推协同优化提升大规模训练效率。

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