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Open nowPosted 93 days ago

Super Sparks-校招-大语言模型后训练/Agentic算法工程师

nio/campus870 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowSuper Sparks-校招-大语言模型后训练/Agentic算法工程师nio/campus · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% 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. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 93 days ago

The posting

-面向智能座舱与车载交互场景，探索大语言模型与 Agent 在真实产品中的应用，负责车载对话系统、任务型助手、多轮交互Agent等方向的研究、训练优化、效果评估与产品迭代。 -跟踪并复现 LLM 后训练与 Agentic Training 前沿技术，包括 On-policy Distillation、Agentic RL、RLHF / RLVR 等，推动其在车载智能体、复杂任务执行、多工具调用和多轮推理决策中的落地。 -参与构建 Agent 能力增强所需的训练与交互环境，包括 Tool 系统、User Simulator、World Model、Reward Function、Agent Harness、任务编排与自动化评测框架等。 -基于实车交互数据和线上反馈，进行问题挖掘、错误归因与方案优化，建设数据生产、模型训练、离线评测、在线分析和失败案例回流的闭环体系，推动大模型能力从“可用”走向“好用、可靠、可持续进化”。

-博士及以上学历，计算机、自动化、信号处理等相关专业 -熟悉 LLM Agent 与 Agent Harness 技术体系，理解 ReAct、Function Calling、Tool Integrated Reasoning、Reflection、Memory、Planning、多轮交互等范式。 -熟悉 PPO、GRPO、DAPO 等 LLM RL 算法原理，了解 Agentic RL、长程任务学习、稀疏奖励建模、轨迹级优化等方向。 -熟悉 Verl、Slime、ms-swift、OpenRLHF、TRL 等训练或强化学习框架，了解分布式训练、推理服务和评测 infra 者优先。 -具备较强的论文跟踪、技术复现和工程落地能力，有高水平论文、竞赛名次或开源项目经验者优先。

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