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Open nowPosted 39 days ago

AI Infra Engineer（Agentic RL 训练框架）

xiaopeng/index1,586 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen nowAI Infra Engineer（Agentic RL 训练框架）xiaopeng/index · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. xiaopeng/index postings stay open a median of 1 days.

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 39 days ago

xiaopeng/index median: 1 days open

The posting

参与机器人大模型强化学习训练框架的研发与优化，面向不同模型、训练方式和业务场景，完善训练与推理能力，提升框架的稳定性、扩展性和运行效率。通过框架开发、性能分析与算法工程协作，支撑大规模强化学习实验和业务训练，推动算法能力高效落地。

【职位描述】 1. 训练框架研发：负责强化学习训练框架的设计、开发与迭代，完善 GRPO、PPO、On-Policy Distillation 等训练方式的完整流程、数值正确性与运行稳定性，统一训练数据、优化目标与参数更新的扩展接口； 2. 核心模块与多后端适配：参与训练、推理、轨迹管理、奖励计算等核心模块研发，推进模块解耦与服务化，完善对 FSDP、Megatron、vLLM、SGLang 等训练与推理后端的适配层，支持不同模型、训练方式和业务场景的接入； 3. 评测与性能优化：建设核心训练场景的 benchmark、profiling 与 CI 回归体系，理解不同训练场景的运行特征，定位影响端到端效率与资源利用率的关键瓶颈，结合真实业务负载开展优化并推动成果落地； 4. 业务训练闭环：与算法团队协作完成具身模型 RL 后训练等真实业务的训练闭环，参与训练需求分析、技术方案设计与联调验证，支持新训练方法的接入与工程化落地； 5. 自动化与研发效能：完善任务配置、实验追溯、排障诊断与模型交付工具及文档，降低实验配置、管理与排障成本，提升框架使用体验与算法研发效率。

1. 扎实的 Python 编程与软件工程能力，熟悉 Linux 开发环境，具备良好的代码设计、调试和维护能力； 2. 熟悉 PyTorch，理解大模型训练与推理的基本流程，具有相关框架或基础设施的实际研发经验； 3. 具备分布式训练或推理系统经验，熟悉 FSDP、Megatron、vLLM、SGLang 等框架中的至少一种，能够理解其执行机制并定位实际问题； 4. 具备系统性能分析能力，能够结合 CPU、GPU、内存和通信等运行信息定位瓶颈，并通过实验验证优化效果； 5. 了解强化学习中采样、训练、环境交互和权重更新的关系，能够理解算法需求并转化为合理的工程实现； 6. 具备独立负责模块开发与问题闭环的能力，能够与算法及业务团队有效协作，完成方案设计、开发验证和持续迭代。

【加分项】 1. 有 AReaL、verl、RLinf 等强化学习框架的使用、集成或开发经验； 2. 有训练或推理框架源码开发、多后端适配、分布式通信或显存优化经验； 3. 熟悉 PyTorch Profiler、Nsight 等性能分析工具，或具有 CUDA、Triton、算子优化相关经验； 4. 有多模态、Agentic RL、异步强化学习等场景的工程实践，或有服务化、资源调度和自动调优相关经验。

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