greenhouse
HPC Engineer
AlphaGrep Securities
China, USA
Mid
Discovered 1 weeks ago
bashcpluspluslinuxpython
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岗位职责 / Responsibilities
- 我们正在寻找一位 HPC / GPU 集群工程师,协助设计、运营并持续优化公司用于分布式模型训练及高性能计算的大规模 GPU 计算环境。您将端到端负责集群的性能与稳定性——从 GPU 硬件与互联网络,到存储层、调度系统、监控平台,以及保障数百块加速器高效运行的全套工具链。
- We are looking for an HPC / GPU Cluster Engineer to help design, operate, and continuously optimize our large-scale GPU compute environment used for distributed model training and high-performance workloads. You will own the performance and reliability of the cluster end to end — from the GPUs and interconnect fabric up through the storage layer, scheduler, monitoring, and the tooling that keeps hundreds of accelerators running efficiently.
任职要求 / Qualifications
- 具备生产环境下大规模 GPU 或 HPC 集群的运营经验,能够系统性地识别并解决性能瓶颈。
- Experience operating large GPU or HPC clusters in production, identifying performance bottlenecks and resolving them systematically.
- 具备 RDMA 编程及底层调试的实战经验,包括 RDMA verbs / libibverbs、内存注册、队列对、完成队列、RDMA 读写、发送/接收操作及性能基准测试。
- Hands-on experience with RDMA programming and low-level RDMA debugging, including RDMA verbs / libibverbs, memory registration, queue pairs, completion queues, RDMA read/write, send/recv, and performance benchmarking.
- 熟悉分布式训练的核心底层技术,包括 InfiniBand 和/或 RoCEv2、GPUDirect RDMA、GPUDirect Storage、NCCL、CUDA 驱动及 OFED。
- Practical experience with the technologies underpinning distributed training, including InfiniBand and/or RoCEv2, GPUDirect RDMA, GPUDirect Storage, NCCL, CUDA drivers, and OFED.
- 具备 Slurm 等调度系统的使用经验,包括安装配置、队列/分区管理、作业故障排查及监控。
- Experience with workload schedulers such as Slurm, including setup, configuration, queue/partition management, job troubleshooting, and monitoring.
- 具备高性能共享存储或并行/分布式文件系统的搭建与管理经验,如 Lustre、BeeGFS、WEKA、VAST、DDN/ExaScaler 等。
- Experience setting up and managing high-performance shared storage or parallel/distributed filesystems such as Lustre, BeeGFS, WEKA, VAST, DDN/ExaScaler, or similar systems.
- 熟练掌握 Python、Bash,优先具备 C/C++ 能力,用于集群自动化、诊断、基准测试及监控开发。
- Solid scripting/programming ability in Python, Bash, and preferably C/C++, for cluster automation, diagnostics, benchmarking, and monitoring.
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