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GPU、多节点训练及分布式计算任务，解决数据加载、节点通信、任务并行和故障恢复中的工程问题。\n数据访问与性能优化： 与数据及研究团队协作，优化大规模数据处理、训练数据读取、缓存和存储访问，定位并改善计算、网络与 I/O 瓶颈。\n机器学习平台与研究工具： 建设开发环境、任务提交、实验管理、依赖与镜像管理等平台能力，提升研究过程的可复现性和实验迭代效率。\n训练与推理基础设施： 支持模型训练、批量及在线推理任务的部署和运行，完善环境交付、版本管理、监控及故障恢复流程。\n自动化与稳定性建设： 通过代码和自动化管理基础设施，完善监控告警、权限隔离、资源配额与容量管理，持续提升系统的可靠性和可维护性。\n需求落地与使用体验： 深入理解研究员和工程师的工作流，将共性需求转化为可复用的平台能力，推动工具落地并持续改进使用体验。\n岗位要求\n具备基础设施、平台工程、分布式系统、高性能计算或机器学习基础设施相关技术基础，能够完成模块设计、开发、部署与问题排查。\n扎实的 Linux、网络、存储和操作系统基础，能够分析资源使用情况，定位性能与稳定性问题。\n熟练掌握 Python、Go、C++、Rust、Java 等至少一门编程语言，具备良好的编码习惯和工程意识。\n理解容器、任务调度与资源管理，熟悉 Kubernetes、Slurm、Ray 或同类系统中的至少一种。\n理解 GPU 计算、分布式训练或大规模数据计算中的至少一个方向，了解相关任务对数据 I/O、网络通信和资源调度的要求。\n了解 CI/CD、Infrastructure as Code、监控告警及自动化运维，重视系统的可观测性、可复现性与故障恢复能力。\n具备良好的学习、沟通与协作能力，能够理解研究需求，分析技术取舍并推动方案落地。\n加分项\n有量化投资、对冲基金、机器学习平台或大规模研究计算平台经验。\n有 GPU 集群建设、分布式训练性能调优经验，熟悉 PyTorch Distributed、NCCL、CUDA、RDMA 等相关技术。\n有大规模特征工程、数据管道、分布式存储或 PB 级数据处理经验。\n有模型服务、大模型微调或推理基础设施建设经验。\n有公有云、混合云资源管理与 GPU 成本优化经验。\n熟悉 Claude Code、Codex 等 AI coding 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