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Xiaomi · 北京

# 训练平台性能优化工程师实习生

北京 

Listed on Xiaomi ’s official careers site. Posted January 20, 2026. Check that it is still open and apply there.
[Apply on Xiaomi ’s careers site →](https://xiaomi.jobs.f.mioffice.cn/internship/position/7597339318662908210/detail)

[OpenNash Jobs](https://jobs.opennash.com/) / [Xiaomi ](https://jobs.opennash.com/companies/xiaomi/) / 训练平台性能优化工程师实习生

## Job description

深度优化训练流程 主导模型训练全链路性能分析与优化，设计GPU资源弹性调度策略 开发自动化训练加速工具链，构建可扩展的云端训练框架 研发混合精度训练、梯度压缩等前沿技术，突破训练吞吐瓶颈 构建训练优化体系 制定标准化训练效能评估体系，建立成本-效率量化模型 设计可复用的训练加速组件库，沉淀最佳实践方法论 开发训练过程性能分析平台，实现性能问题智能诊断 赋能业务研发 优化多任务资源调度策略，提升GPU集群整体利用率 为算法团队提供训练加速解决方案，缩短模型迭代周期

## Qualifications
- 精通深度学习训练加速技术，熟悉分布式训练框架设计
- 掌握CUDA编程及GPU性能分析工具（nsys/torch profiler）
- 熟练使用PyTorch框架，具备训练流程优化实战经验
- 熟悉常见模型压缩技术（量化/剪枝/蒸馏）及落地应用
- 具备大规模集群资源调度系统开发经验者优先 【技术加分项】
- 有mmcv//deepspeed/megatron/ray等训练框架开发经验
- 算子优化经验,triton/cuda等开发经验.
- 熟悉MPI/NCCL等分布式通信协议
- 在MLSys/ICLR等顶会发表过训练优化相关论文

## At a glance
- Posted by the employer: Jan 20, 2026
- First seen by OpenNash: Aug 1, 2026
- Last checked: Oct 19, 2026
- Listed for: 272 days
- Location: 北京[Apply on Xiaomi ’s careers site →](https://xiaomi.jobs.f.mioffice.cn/internship/position/7597339318662908210/detail)

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