Cluster Engineer

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STN Incorporated

Site WebLinkedInTous les Emplois

11 - 50 employés

Fondée en 2016

🏢 Entreprise

🔒 Cybersecurity

🔧 Matériel

Enterprise • Cybersecurity • Hardware

STN Incorporated est un fournisseur de services gérés en informatique et d'infrastructure cloud de niveau entreprise, offrant une infrastructure sécurisée et prête pour l'audit pour les systèmes critiques pour les entreprises et les charges de travail exigeantes en intelligence artificielle. STN opère un modèle de fonctionnement géré offrant des clouds CPU privés, une infrastructure GPU One AI, un réseau et un stockage sécurisés, ainsi qu'une assistance humaine 24/7 avec des SLA de haute disponibilité. Leurs services incluent l'infrastructure gérée et les opérations cloud, les opérations de cybersécurité et la réponse aux incidents, la gestion de la conformité et des risques (SOC 2 Type II, compatible HIPAA), la sauvegarde et la récupération, et l'acquisition de technologies d'entreprise ainsi que la gestion du cycle de vie. STN sert les entreprises, les entreprises SaaS en forte croissance, les constructeurs d'IA et les développeurs de modèles, les entreprises de robotique/IA physique et les industries réglementées telles que la santé.

Description

• Design, deploy, and optimize multi-node GPU clusters for AI training and inference workloads • Tune distributed training environments to maximize GPU utilization, throughput, and scaling efficiency • Optimize inference clusters for token generation throughput, low latency, and high GPU utilization • Build and support production AI infrastructure running hundreds to thousands of GPUs • Analyze and eliminate performance bottlenecks across compute, networking, storage, and software layers • Perform NCCL benchmarking, analysis, and tuning for collective communication performance • Design and optimize GPU networking using InfiniBand or RoCE v2, including RDMA, congestion management, topology awareness, and QoS • Configure and tune distributed AI software stacks including PyTorch, NCCL, CUDA, UCX, MPI, Slurm, and Pyxis/Enroot • Optimize GPU scheduling and resource allocation for training and inference environments • Develop benchmarking and validation processes for hardware, firmware, drivers, and software releases • Identify performance regressions and troubleshoot distributed training issues at scale • Optimize storage architectures for checkpointing, dataset streaming, and high-performance parallel I/O • Work closely with ML engineers to improve training scalability and inference efficiency • Create automation to deploy, validate, benchmark, and monitor GPU clusters • Evaluate emerging AI infrastructure technologies and recommend platform architecture improvements

🎯 Exigences

• 7+ years designing or operating large-scale Linux infrastructure • 5+ years supporting production GPU clusters for AI or HPC workloads • Experience building multi-node GPU training environments from the ground up • Deep expertise with distributed PyTorch training • Extensive experience troubleshooting and optimizing NCCL communications • Understanding of AllReduce, ReduceScatter, AllGather, Broadcast, and point-to-point communications • Experience benchmarking distributed training with nccl-tests, NVIDIA DCGM, and Nsight Systems; MLPerf preferred • Understanding of GPU memory management, KV Cache, activation checkpointing, tensor parallelism, pipeline parallelism, and data parallelism • Experience optimizing LLM inference throughput, including tokens/sec, batch sizing, continuous batching, KV cache, and memory bandwidth • Experience tuning CUDA, NCCL, UCX, and MPI • Expert-level Linux systems administration skills • Experience with Slurm • Experience using Pyxis and Enroot for containerized GPU workloads • Strong Python and Bash scripting skills • Strong understanding of InfiniBand, RoCE v2, RDMA, GPUDirect RDMA, GPUDirect Storage, UCX, MPI, network topology optimization, congestion control, QoS, ECN/PFC, and high-speed Ethernet • Experience designing or tuning AI storage, including parallel file systems, distributed storage, object storage, NVMe, checkpoint optimization, dataset staging, storage bandwidth, and metadata performance • Experience with NCCL benchmarking, multi-node scaling analysis, GPU utilization optimization, communication/computation overlap, NUMA optimization, CPU affinity, PCIe topology, GPU topology, memory bandwidth analysis, and end-to-end performance profiling • Preferred: Kubernetes, NVIDIA GPU Operator, Kubernetes batch scheduling, vLLM/TensorRT-LLM/SGLang, NVIDIA DGX SuperPOD or similar, MLPerf, Prometheus/Grafana/DCGM Exporter, Ansible/Terraform, and AWS/Azure/GCP GPU environments