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Open nowPosted 255 days ago

算法工程师 - LLM后训练

kaonai/index37 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now算法工程师 - LLM后训练kaonai/index · Beijing, Chinese Mainland

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Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 255 days ago

The posting

参与公司AI算法相关工作，专注于提升大语言模型（LLM）的生成质量，推动技术创新和产品落地，具体职责包括但不限于： 1. 监督微调：深入研究和实施大语言模型（LLM）的SFT、RFT等技术方案，优化AI的情感表达能力和剧情设计能力，突破长期记忆、行为一致性等技术难点 2. 奖励模型：基于海量用户交互日志和反馈数据，深入挖掘真实用户偏好信号，构建高质量的偏好数据集；设计并实现包括生成式奖励模型（GRM）、过程奖励模型（PRM）等在内的多维度奖励函数 3. 强化学习：深入理解PPO、DPO、GRPO等常用LLM强化学习方法，结合具体业务目标（如上下文一致性、逻辑正确性、OOC等），设计定制化的奖励组合与训练策略 4. Agent：面向复杂、多步骤的业务场景，设计并实现基于大语言模型的Agent架构；集成工具调用（Tool Use）、记忆机制（Memory）、反思（Reflection）、多智能体协作等范式实现复杂任务的能力跃迁

1. 优秀的代码能力、数据结构和基础算法功底，熟练C/C++或Python，ACM/ICPC、NOI/IOI、Top Coder、Kaggle等比赛获奖者优先； 2. 熟悉NLP、CV相关的算法和技术，熟悉大模型训练、RL算法者优先； 3. 在虚拟角色生成、角色扮演、情感陪伴等方向有项目经验或论文成果者优先； 4. 能系统性拆解角色类模型在生成一致性、长期记忆等场景的挑战，提出创新解决方案； 5. 具备较强自驱力，能与产品、算法团队紧密协作，推动技术从研究到落地的全链路闭环。

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