Kimi · 月之暗面Post-Training Algorithm Researcher/Engineer
全职 · 算法与研究
需要对标的能力
大模型原理 · LLM 基础Agent · 智能体设计模型微调 · Post-TrainingAI Infra · 推理部署
岗位职责与要求
Job Description:
1. Deeply participate in data processing, cleaning, and optimization for CodeLLM; scientifically improve, filter, and balance the data quality used in LLM pre-training and alignment to enhance the model’s code and agent capabilities during the Pretrain and SFT stages.
2. Actively engage in improving Coding/SWE Agent capabilities through SFT (Supervised Fine-Tuning), RL (Reinforcement Learning), and building/scaling tools, Docker environments, and tasks; personally gain know-how on how to effectively train an agentic model.
3. Deeply explore methods to enhance the model’s problem-solving ability, aiming for: 1) Nearly 100% accuracy on simple and medium-level algorithm problems; 2) Maximizing the solving rate of difficult algorithm problems (IOI/ICPC level), pushing the model’s intelligence upper bound.
4. Improve user problem-solving rates and satisfaction on Code/SWE-related questions, making the model genuinely easy and pleasant to use.
Job Requirements:
1. Bachelor’s degree or above, preferably in Computer Science, Artificial Intelligence, Automation, Mathematics, or related fields.
2. Solid programming foundation, deep understanding of data structures and algorithm design; proficient in one or more mainstream programming languages such as Python and C++; skilled in deep learning frameworks and libraries like PyTorch, TensorFlow, and Hugging Face.
3. Rich hands-on experience in large model pre-training and alignment training, or experience in Code/SWE optimization is preferred.
4. Publication in top-tier academic conferences or outstanding achievements in machine learning and AI fields are preferred.
5. Algorithm competition award experience (e.g., ACM, IOI, NOI, Top Coder) is a plus.
6. Strong sense of responsibility, proactive attitude, and good communication and teamwork skills.
查看中文参考译文
职位描述：
1. 深度参与 CodeLLM 的数据处理、清洗与优化工作；科学性地改进、筛选并平衡 LLM pre-training 和 alignment 阶段使用的数据质量，以提升模型在 Pretrain 和 SFT 阶段的代码与 agent 能力。
2. 积极参与通过 SFT (Supervised Fine-Tuning)、RL (Reinforcement Learning) 以及构建/扩展工具、Docker 环境和任务来提升 Coding/SWE Agent 能力；亲身掌握如何有效训练 agentic model 的实践经验。
3. 深入探索提升模型解题能力的方法，目标为：1) 在简单和中等难度算法题上实现接近 100% 的准确率；2) 最大化困难算法题（IOI/ICPC 级别）的解决率，推动模型智能上限。
4. 提升用户在 Code/SWE 相关问题上的解题率和满意度，使模型真正变得易用且好用。
职位要求：
1. 本科及以上学历，计算机科学、人工智能、自动化、数学或相关专业优先。
2. 扎实的编程基础，深入理解数据结构与算法设计；熟练掌握 Python、C++ 等一种或多种主流编程语言；熟练使用 PyTorch、TensorFlow、Hugging Face 等深度学习框架和库。
3. 具备丰富的大模型 pre-training 和 alignment training 实践经验，或有 Code/SWE 优化经验者优先。
4. 在顶级学术会议发表论文，或在机器学习及 AI 领域有突出成果者优先。
5. 有算法竞赛获奖经历（如 ACM、IOI、NOI、Top Coder）者优先。
6. 责任心强，工作积极主动，具备良好的沟通能力和团队协作精神。
译文仅供辅助阅读，具体要求以原始招聘来源为准。