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Open nowPosted 178 days ago

Agentic算法工程师/研究员

gigaai0118/index63 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAgentic算法工程师/研究员gigaai0118/index · Beijing, Chinese Mainland

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12.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 178 days ago

The posting

岗位职责 - 负责 Agentic系统设计与研发，包括任务规划、分层决策、工具调用、记忆机制、长时程推理等核心模块。 - 研究并落地Agentic Reinforcement Learning相关方法，包括但不限于基于环境交互的策略优化、tool-use RL、多阶段训练、self-play / curriculum learning / outcome reward 等方向。 - 推动Agent for Embodiment的关键能力建设，探索Agent与具身基础模型、VLA、世界模型、运控模块之间的协同方式。 - 搭建和优化训练、评测与数据闭环，包括仿真环境接入、任务构造、奖励设计、轨迹采集、benchmark评估与 failure analysis。 - 推动前沿研究成果在真实业务和机器人系统中的落地。 - 与模型、数据、仿真、机器人控制等团队协同，推动从算法到系统的端到端优化。

岗位要求 - 计算机、自动化、人工智能、机器人等相关专业，硕士及以上学历。 - 对Agent方向有较深入理解，熟悉至少一个重点方向： - Agentic Reinforcement Learning - LLM Agent / Tool Use / Planning - Embodied Agent / Robot Agent - 理解感知-决策-执行闭环，了解 VLA、世界模型、分层控制、多模态决策中的一种或多种技术路线。 - 具备扎实的算法与工程能力，能够独立完成系统搭建、实验设计与性能优化。 - 代码能力强，熟练使用 Python ，熟悉 PyTorch ，具备良好的工程习惯；有较强的训练框架、数据 pipeline、实验平台开发能力。 - 熟悉强化学习或序列决策中的一个或多个方向，如 PPO / GRPO / DPO / Offline RL / RLHF / RLVR 等。

加分项 - 有 agentic RL、tool-use、multi-agent、world model、VLA、imitation learning、planning 等相关研究或开源经历。 - 在顶会/顶刊发表过相关论文，或有高质量开源项目、竞赛成绩。 - 对真实机器人系统有理解和实际操作经验

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