NVIDIA is looking for a Senior Solutions Architect, Generative AI with deep, hands-on experience in large-scale GPU systems.

This role involves working with some of the world’s leading consumer internet companies and frontier labs building foundation models.

Primary responsibilities include:

The ideal candidate will have:

6+ years of experience in AI infrastructure, systems engineering, high-performance computing, networking, site reliability engineering, or a related technical role
Deep understanding of Linux systems, distributed computing, GPU architectures, and the hardware and software components of large-scale AI clusters
Hands-on experience designing, deploying, operating, or troubleshooting high-performance GPU networks in on-premises or cloud environments

Responsibilities:

Collaborate with customers to maximize GPU utilization and end-to-end workload throughput while improving infrastructure reliability and reducing infrastructure costs
Design and optimize large-scale AI clusters across GPU compute, high-performance networking, storage, workload scheduling, orchestration, and observability
Profile distributed training and inference workloads to identify bottlenecks across GPUs, CPUs, memory, network fabrics, storage systems, and software stack
Diagnose complex infrastructure and distributed systems issues spanning InfiniBand and RoCE fabrics, cloud interconnects, RDMA, NCCL, NVLink, and NVSwitch
Lead proof-of-concepts and performance studies for large-scale AI infrastructure, developing benchmarking tools, automation, runbooks, and technical collateral as needed
Partner with NVIDIA’s engineering, product, and sales teams to secure design wins and drive innovative solutions based on customer requirements and field feedback

Requirements:

BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or another Engineering field, or equivalent experience
6+ years of experience in AI infrastructure, systems engineering, high-performance computing, networking, site reliability engineering, or a related technical role
Deep understanding of Linux systems, distributed computing, GPU architectures, and the hardware and software components of large-scale AI clusters
Hands-on experience designing, deploying, operating, or troubleshooting high-performance GPU networks in on-premises or cloud environments using technologies such as InfiniBand, RoCE, or GPUDirect RDMA
Experience debugging NCCL communication and distributed collective performance, including topology, transport, congestion, routing, and host-level configuration issues
Experience profiling AI workloads and identifying performance bottlenecks across compute, networking, storage, and orchestration layers
Experience with cluster schedulers and orchestration platforms such as Kubernetes and Slurm, along with containers and production monitoring systems
Proficiency with Python, shell scripting, or similar languages for infrastructure automation, benchmarking, and systems troubleshooting

Nice to Have:

Experience architecting and operating large-scale production GPU clusters for distributed training or inference
Deep expertise with NVIDIA infrastructure technologies such as DGX/HGX systems, NVLink, NVSwitch, NCCL, InfiniBand, and Spectrum-X
Hands-on experience using tools and telemetry such as NCCL tests, DCGM, Nsight Systems, fabric counters, and host- or switch-level diagnostics to isolate performance and reliability issues
Understanding of network topology, congestion control, collective communication patterns, and their impact on distributed AI workload performance
Experience optimizing storage and data pipelines to sustain high-throughput training and inference workloads

Benefits:

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Senior Solutions Architect, Generative AI

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