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Open nowPosted 8 hours ago

RL 训练与推理工程师-多模态生成方向

vrfi1sk8a0/index205 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowRL 训练与推理工程师-多模态生成方向vrfi1sk8a0/index · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

13.1% of postings close within 7 days. Measured by our own scanner across the market. vrfi1sk8a0/index postings stay open a median of 5 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.1%7 days
  4. 21.1%14 days
  5. 35.8%30 days
  6. 59.0%60 days
This job: posted 8 hours ago

vrfi1sk8a0/index median: 5 days open

The posting

1. 负责视频生成模型的强化学习训练与推理基础设施建设，打通样本生成（rollout）、奖励计算、策略更新和效果评估链路，支撑模型持续迭代。 2. 优化视频生成模型的推理与 rollout 性能，围绕 DiT、Diffusion / Flow Matching 等模型的计算特点，提升采样吞吐、降低显存占用和生成成本。 3. 建设和优化分布式 RL 训练框架，解决长视频、高分辨率及多样本采样场景下的计算、通信和存储瓶颈，提升 GPU 利用率与集群扩展效率。 4. 优化训练、推理与奖励模型之间的协同机制，包括任务调度、资源分配、权重同步、数据传输及流水线并行，提升端到端训练效率和稳定性。 5. 与算法团队合作，推动强化学习、偏好优化及模型加速方案落地；建立性能分析、数值正确性验证、监控与故障恢复机制，保障实验可复现和训练可靠运行。

1. 具备扎实的计算机基础，熟练掌握 Python，熟悉 PyTorch 等深度学习框架，具备良好的工程设计、代码实现及问题定位能力。 2. 在分布式训练、模型推理优化或 RL 系统中至少一个方向有深入实践，能够独立分析性能瓶颈并推动优化落地。 3. 熟悉 GPU 计算与显存管理，理解混合精度、梯度累积、激活重计算、数据并行、模型并行等技术，具备多机多卡训练或推理经验。 4. 理解强化学习训练的基本流程，了解 rollout、奖励计算、策略更新及权重同步之间的关系，能够与算法团队协作解决效率、稳定性和数值一致性问题。 5. 了解 Diffusion、Flow Matching 或 DiT 等生成模型的基本原理，对视频生成中的长序列计算、时序建模及多步采样有一定认识。 6. 具备较强的主动性和协作能力，能够从模型迭代需求出发，在效果、速度、成本和工程复杂度之间做出合理取舍。 加分项 1. 有图像或视频生成模型的训练、推理加速、RL 后训练或偏好优化经验。 2. 有 CUDA / Triton 算子开发、Attention 优化、序列并行或通信与计算重叠优化经验。 3. 有大规模 RL 系统建设经验，实践过异步 rollout、训推资源协同、奖励模型服务化或故障恢复。 4. 有模型蒸馏、少步采样、量化、稀疏计算等优化经验，或相关开源项目的核心贡献。

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