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Open nowPosted 283 days ago

端到端视频编解码实习生-音视频技术

ByteDance19,258 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now端到端视频编解码实习生-音视频技术ByteDance · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.4% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 283 days ago

ByteDance median: 23 days open

The posting

ByteIntern：面向2027届毕业生（2026年9月-2027年8月期间毕业），为符合岗位要求的同学提供转正机会。 团队介绍：视频与边缘部门承载了字节跳动的媒体内容分发基建及技术中台，支持了字节全系产品，如抖音、今日头条、番茄小说、西瓜视频等APP的点播、直播、实时通信、图片等多媒体业务发展，同时将业务发展过程中沉淀下来的技术能力和工具，通过火山引擎对外输出，面向各行各业用户提供视频云产品和服务，愿景是为内外部业务伙伴提供最低成本、最优画质、最低延时、最安全可靠的富媒体内容分发解决方案，助力业务伙伴降本提效实现持续增长。

1、端到端视频压缩框架研究与实现：参与设计并实现高压缩率、低复杂度的端到端压缩网络，攻坚1080p视频实时解码，并以压缩性能超越H.266/VVC为目标； 2、模型设计与性能优化：围绕主流研究方向，研究部署友好的时空上下文建模、码率控制与熵编码结构设计，持续提升端到端压缩收益； 3、模型高性能推理优化：探索量化感知训练、算子融合等技术，极致优化模型推理效率、并行计算与存储访问，使神经网络模型能高效运行在目标硬件上； 4、系统化评测与分析：构建系统化的编解码器评测流水线，对方案进行模块级复杂度评估和全面的性能分析，识别并优化端到端链路中的关键瓶颈，实现率-失真-复杂度的联合优化。

1、2027届硕士及以上学位在读，计算机科学与技术、电子信息工程、通信工程、应用数学等相关专业优先； 2、熟练掌握Python与至少一种深度学习框架（如PyTorch/TensorFlow），具备独立设计、训练和调试复杂神经网络的能力； 3、理解深度学习基本原理，熟悉CNN、Transformer等主流网络结构；对视频/图像压缩、信号处理、信息论有基本的理论储备； 4、有实际的深度学习项目经验，能够独立完成模型的设计、训练、调试与评估； 5、对视频编码与深度学习的交叉领域怀有浓厚兴趣，有持续追踪和学习前沿技术的热情与主动性； 6、具备优秀的逻辑思维与问题分析能力，能够系统性地定位、分析并解决科研或工程中遇到的问题。

加分项： 1、在相关领域顶级会议/期刊发表过论文，或有开源项目贡献经验； 2、熟悉JPEG-AI/EEE1857/MPEG-VCM等端到端压缩标准或H.265、H.266等视频编码标准； 3、具备GPU/NPU平台的性能优化经验。

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