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Open nowPosted 33 days ago

【校招】大模型智能体算法青年研究员（能力集成方向）-智能体系统中心

aicarrier/522799209 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【校招】大模型智能体算法青年研究员（能力集成方向）-智能体系统中心aicarrier/522799 · Shanghai, Chinese Mainland

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Share of postings closed within
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  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 33 days ago

The posting

1. 面向科学领域的论文、技术报告、专业文档、知识库等异构数据，参与领域任务轨迹、数据集及 Benchmark 的构建，建设可规模化的 Agent 训练环境与评测体系。 2. 聚焦高价值科学领域，提升大模型的 Agentic 能力，使模型在高复杂度任务中的表现达到行业前沿水平。重点提升模型在科学领域长程任务、多工具调用、真实环境交互中的规划、执行与反思能力。 3. 探索面向长程、多轮、稀疏奖励场景的后训练算法，系统性研究并解决信用分配、训练稳定性、探索效率等核心问题，形成可复用的方法论与训练策略。 4. 探索“AI 加速 AI 研发”的新范式，让模型参与递归数据生成、模型后训练机制迭代等研发流程，形成可递归的能力增益回路。 5. 从复杂科学任务和真实 Agent 训练问题中提炼关键研究问题，能够深入分析长程决策、多工具协同、环境反馈、奖励设计等底层机制，并提出具有深度和前瞻性的解决方案。

1. 具备扎实的机器学习与深度学习基础，对大模型训练流程，尤其是后训练阶段有深度理解。 2. 具备较强的研究能力、逻辑分析能力、数据敏锐度和算法实现能力，能够将算法思路高质量落地到实际训练与评测系统中。 3. 理解并能够解决大模型后训练中的关键问题，包括但不限于 Reward Hacking、Training Stability、Exploration Efficiency、长程信用分配、环境噪声、非 policy 负向 reward、训推一致性等真实工程挑战。 4. 具备从复杂现象中抽象关键问题的能力，能够围绕长程任务、稀疏奖励、环境交互、科学推理等方向进行系统性思考，并提出具有研究深度的技术路线。 5. 具备良好的学术表达和跨团队协作能力，能够清晰阐述研究动机、方法设计与实验结论，并推动研究成果在工程系统中落地。 6. 具备至少一种主流深度学习框架的工程实现能力，能够独立完成算法设计、实验验证、问题定位和模型迭代。

加分项 1. 在 NeurIPS、ICLR、ICML、CVPR、ACL 等顶级会议发表过高水平研究成果，或在相关方向有系统性研究积累者优先。 2. 主导或深度参与过知名开源项目，且项目具有一定社区影响力者优先。 3. 熟悉大规模后训练流程者优先，包括 rollout / 采样加速、sandbox、trajectory replay、训推框架等。 4. 具备 Agent harness、可验证环境构建、复杂任务评测系统搭建经验者优先。 5. 在科学领域大模型、科学推理、工具调用、多轮规划、RL post-training、自动化研究流程等方向有深入研究成果者优先。

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