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Open nowPosted 208 days ago

多模态理解生成强化学习研究员-Seed大模型人才校招

ByteDance19,257 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now多模态理解生成强化学习研究员-Seed大模型人才校招ByteDance · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 16 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 208 days ago

ByteDance median: 16 days open

The posting

Seed 大模型人才校招，是字节跳动 Seed 面向高校人才推出的招聘项目。我们始终相信，真正重要的技术进步来自对高难度问题的持续挑战。面对 AI 时代的巨大机遇，Seed 团队并不止步于模型迭代，而是选择进入技术深水区，推进下一代 AI 范式突破，不断探索智能的边界与上限。

团队介绍：字节跳动 Seed 团队成立于 2023 年，致力于寻找通用智能的新方法，追求智能上限，为科技和社会发展作出贡献。 Seed 团队在 AI 领域拥有长期愿景与决心，团队研究方向涵盖 MLLM、GenMedia、AI for Science、机器人等，在中国、新加坡、美国等地设有实验室和岗位。目前，团队已推出业界领先的通用大模型以及前沿的多模态能力，支持豆包、即梦、TRAE 等超过 50 个应用场景，并通过火山引擎开放给企业客户。第三方数据显示，豆包 App 用户量在中国市场排名第一，豆包大模型日均 Token 调用量行业领先。

1、研究并搭建适配多模态理解生成场景的Agent RL技术体系；以RL驱动生成全流程的智能决策，解决复杂指令、长时序创作、交互式创作等场景下的技术问题，利用Agent 能力深度串联模型的感知理解与高质量生成，拓展多模态模型的生成能力边界与场景适配性； 2、负责图像、视频、世界模型等多模态场景下的RL算法端到端全流程研发，搭建稳定、可复现、可扩展的RL训练Pipeline；跟进并探索前沿RL算法，完成算法在多模态生成、长时序逻辑交互场景的适配、调优与工程落地； 3、参与多模态理解生成模型核心模块的RL技术创新探索，研究多模块联合优化的RL算法框架；持续迭代Reward设计体系，针对性优化多模态生成模型的核心效果指标，助力提升生成内容质量与用户使用体验； 4、参与RL技术基建与工具链的持续优化：迭代多模态Reward Model架构，完善RL训练数据基建，制定数据标注规范，搭建标注流程与数据有效性验证链路；优化RL训练与推理环节的显存占用、计算效率，提升训练吞吐量与技术落地效率； 5、持续跟踪全球RL与多模态生成领域的前沿技术进展，参与推动算法创新与落地，重点探索长视频生成、UG场景RL规模化落地、真实物理规律建模等关键方向的技术解法。

1、2027届获得本科及以上学历，计算机、电子、数学等相关专业； 2、具备扎实的算法与编程基础，熟练掌握强化学习、深度学习核心理论，有AIGC（图像/视频/多模态生成）领域或Agent领域的研究或工业落地实践经验； 3、熟悉多模态生成模型的核心架构，熟悉GRPO、PPO、DPO等主流强化学习算法，有Reward Model搭建与优化相关经验者优先； 4、能够独立完成算法从方案设计、代码实现到落地验证的全流程工作，有大规模分布式训练与优化相关经验，能针对性解决训练过程中的显存、性能等常见问题； 5、发表过高质量论文，或有高质量开源项目经验者优先；具备良好的团队协作意识与跨部门沟通能力。

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