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Open nowPosted 186 days ago

算法工程师 - 视频生成

ponyai/ponyai76 open roles

Where
Guangzhou, Guangdong, Chinese Mainland
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Your applicationOpen now算法工程师 - 视频生成ponyai/ponyai · Guangzhou, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. ponyai/ponyai postings stay open a median of 4 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: posted 186 days ago

ponyai/ponyai median: 4 days open

The posting

1. 基础模型研发：负责视频生成基础模型（Text-to-Video, Image-to-Video, Video Inpainting 等）的核心算法研发、架构探索与生成效果迭代。 2. 全链路训练与优化：深入参与生成模型的全生命周期研发，包括大规模预训练、SFT、人类偏好对齐（RLHF/DPO 等强化学习方法）。追求生成内容的真实感（Photorealism），大幅提升模型对客观物理规律的理解、时空连贯性以及复杂真实物理世界的还原能力（打造 Physical World Simulator 级别效果）。 3. 数据闭环与清洗：搭建并完善高质量视频数据工程流水线，主导基于多模态大模型（VLM）的海量视频数据自动化清洗、多维度标注、过滤与质量评估，构建高质量的训练数据集。 4. 推理加速与落地：结合业务需求，开展模型轻量化、显存优化及推理加速工作，提升云端推理吞吐效率与生成速度，保障算法的高效工程落地。

1. 计算机/人工智能/数学等相关专业，本科及以上学历；熟练掌握 Python 及常用数据结构，具备扎实的算法底子与优秀的工程实现能力。 2. 熟悉主流深度学习框架（PyTorch），具备大规模分布式训练（如 DeepSpeed, Megatron, FSDP）的实操、调参及系统性的踩坑经验。 3. 掌握主流生成式视觉算法与基础理论：深刻理解 Diffusion Models（扩散模型）、Transformer/DiT 架构原理，熟悉主流图像/视频生成算法及底座。 4. 具备极强的数据敏感度，熟悉多模态大模型（如 Qwen-VL, GPT-4o 等）的应用，对复杂真实场景数据的提纯、清洗规则设计和 Prompt 体系构建有实战经验。 5. 强烈的工程落地导向，关注生成内容的逼真程度、动态细节及物理合理性，熟悉相关质量指标（如 FVD, 运动平滑度评估），能独立解析和修改前沿开源代码。 6. 具备快速学习能力与良好的沟通协作能力，有技术极客精神。

加分项 - 具备从零到一主导或深度参与大规模视频生成/图像生成基础模型训练经验者优先。 - 在生成模型的强化学习（RLHF/DPO）与真实偏好对齐方向有深入研究或实际落地经验者优先。 - 有自动驾驶仿真、世界模型（World Models）或物理世界模拟视频生成相关研发经验者极大加分。 - 在开源社区活跃，对知名生成式项目有过核心代码贡献者优先。 - 在 CVPR, ICCV, ECCV, NeurIPS 等顶级会议发表过生成方向高质量论文者优先。

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