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Tencent Holdings · Beijing

# 混元大语言模型后训练算法工程师（深圳/北京/上海）

Beijing 

Listed on Tencent Holdings’s official careers site. Listed as of October 19, 2026. Check that it is still open and apply there.
[Apply on Tencent Holdings’s careers site →](https://careers.tencent.com/en-us/jobdesc.html?postId=2009467972866957312)

[OpenNash Jobs](https://jobs.opennash.com/) / [Tencent Holdings](https://jobs.opennash.com/companies/tencent-holdings/) / 混元大语言模型后训练算法工程师（深圳/北京/上海）

## Job description

### Responsibilities
- 负责大语言模型后训练（Post-Training）阶段的核心技术研发，构建和优化高质量的奖励系统（Reward System），通过Reward Modeling (RM) 和强化学习（RL）算法持续提升模型在复杂指令遵循、逻辑推理及价值观对齐方面的能力；
- 深入研究和优化 RLHF 等后训练算法，提升模型训练的稳定性和最终效果；
- 负责大模型个性化（Personalization）与长期记忆（Memory）机制的算法研发，构建精准的“千人千面”用户建模体系，探索模型如何理解、提取、记忆并动态适应不同用户的长期偏好，持续提升个性化交互体验；
- 负责后训练阶段的数据合成与管理，设计高效的数据飞轮机制，利用SFT、Self-Instruct等技术合成高质量训练数据，并负责建立从用户多维反馈（User Feedback）到模型迭代的闭环信号建模体系；
- 负责后训练模型的全维度评测与分析，制定科学的评价指标，跟进前沿技术动态，将最新研究成果快速转化为业务价值。 Requirements
- 计算机科学/软件工程/人工智能等相关专业硕士及以上学历；
- 深入理解 Transformer 架构及大语言模型训练原理，在 LLM Alignment、RLHF、Reward Modeling、 个性化大模型（Personalized LLM）、记忆机制（Memory/RAG） 等后训练领域之一有深入的研究和实践经验；
- 在用户画像建模、推荐系统与大模型结合、或超长上下文/长期记忆模型优化方面有丰富实战经验者优先；
- 具备扎实的算法基础和工程实现能力，熟练掌握 Python，熟悉 PyTorch/TensorFlow 等深度学习框架；
- 拥有分布式训练实战经验，熟悉 Megatron-LM, DeepSpeed, vLLM 等大规模训练及推理框架，有百亿/千亿参数模型训练或调优经验者优先；
- 具备优秀的科研素养，有高质量论文发表（NeurIPS, ICLR, ICML, ACL, EMNLP 等）或在开源社区（如 HuggingFace）有高影响力项目贡献者优先；
- 具备强烈的技术热情和自驱力，善于分析和解决复杂问题，拥有良好的团队协作和沟通能力。

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Jul 19, 2026
- Last checked: Oct 19, 2026
- Listed for: 92 days
- Location: Beijing[Apply on Tencent Holdings’s careers site →](https://careers.tencent.com/en-us/jobdesc.html?postId=2009467972866957312)

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