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Open nowPosted 2 days ago

机器学习系统研发实习生-2028 届

vrfi1sk8a0/379481106 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now机器学习系统研发实习生-2028 届vrfi1sk8a0/379481 · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

13.7% of postings close within 7 days. Measured by our own scanner across the market. vrfi1sk8a0/379481 postings stay open a median of 16 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.8%3 days
  3. 13.7%7 days
  4. 22.1%14 days
  5. 37.6%30 days
  6. 59.1%60 days
This job: posted 2 days ago

vrfi1sk8a0/379481 median: 16 days open

The posting

- 支撑大规模模型研发，需要解决任务排队、GPU 利用率不足、分布式故障等一系列系统问题。 - 我们希望让算法同学更快开始实验，让训练任务稳定运行，也让每一份算力发挥更大价值。你将参与 MiniMax 机器学习平台研发，深入任务调度、资源管理与分布式系统。 你将参与的工作 1. 任务平台研发：开发训练、评测与推理任务的提交、编排、运行和结果管理功能，支持多阶段实验流程。 2. GPU 资源调度：参与 Kubernetes、Ray 等系统上的资源管理、队列与调度研发，解决资源碎片、排队时间长和利用率不足等问题。 3. 分布式任务可靠性：完善任务依赖、状态同步、重试和故障恢复机制，提升长时间运行任务与多阶段任务的稳定性。 4. 研发工具与可观测性：建设实验、模型和数据集管理工具，完善日志、指标及问题诊断能力，让算法同学更方便地开展实验和排查问题。

我们期待你 1. 数据结构、算法、操作系统与网络基础扎实，熟练掌握 Go、Python、C/C++ 中至少一种语言。 2. 熟悉 Linux 开发环境，对分布式系统有基本理解，能分析并发、状态一致性、超时与故障恢复等问题。 3. 有后端或系统项目经验，能完成模块设计、编码与调试，并讲清楚方案选择和设计取舍。 4. 对 AI Infra 和云原生技术有兴趣，愿意深入源码和实际使用场景，持续改进系统稳定性与开发体验。 加分项 • 有 Kubernetes、Ray、Controller/Operator、资源调度或工作流编排经验。 • 了解 PyTorch、JAX 等框架，实践过分布式训练、模型服务或 GPU 资源管理。 • 有分布式系统、容错、性能分析相关研究、开源贡献或完整项目经验。 你将收获 了解大模型研发平台如何管理算力、组织实验和处理故障，在真实任务中打磨系统设计能力，做出算法团队日常使用的平台功能。

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