Job Description

【智谱星】26届校招-AI院-强化学习训练框架工程师北京正式互联网 / 电子 / 网游 - 研发职位描述1. 负责强化学习训练框架的研发、优化和维护，根据业务需求持续改进训练框架和策略，提升模型训练效率；
2. 分析和定位训练中的性能瓶颈，实施针对性优化措施，提升训练效率和稳定性；
3. 跟进业界技术进展，不断同步与集成最新训练优化策略。职位要求1. 26年应届生，硕士及以上学历，计算机相关专业，HPC&MLSys 相关研究领域；
2. 对自然语言处理、计算机视觉和多模态算法有深入理解，熟悉主流的 LLM 模型架构，有分布式训练经验；
3. 对常见RL训练算法有基本了解；
【加分项】
1. 熟悉vllm或sglang等常用开源推理框架。
更多信息：团队工作介绍
【GLM-4.6: Advanced Agentic, Reasoning and Coding Capabilies】
We are releasing the latest version of our flagship model: GLM-4.6. Compared with GLM-4.5, this generation brings several key improvements:
Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.
Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.
【slime: An SGLang-Native Post-Training Framework for RL Scaling】
https://lmsys.org/blog/2025-07-09-slime/
We believe in RL. We believe RL is the final piece toward AGI.
The journey of RL scaling has just begun, and slime is continuously evolving. In the next phase, we will focus on:
1. Collaborating with the SGLang team to explore optimal RL training strategies for large-scale MoE models.
2. Supporting broader post-training workflows, strengthening the pre-training-to-production bridge. 投递