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Senior Solutions Architect, Generative AI

NVIDIA· Architect, Solutions
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Hybrid (Santa Clara, CA), Hybrid (Remote), Hybrid (Remote (WA))$184k - $287.5kFull-timePosted 2mo ago

About the role

AI Solutions Architect with hands-on experience in large-scale GPU systems, focusing on accelerating workloads, designing high-performance AI infrastructure, and leading technical engagements around NVIDIA technologies for consumer internet companies and frontier labs.

Responsibilities

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

Qualifications

  • 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 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.

Skills

Required:GPU-focused AI infrastructure designNCCL, InfiniBand, RoCE, NVLink, NVSwitchCluster schedulers and orchestration (Kubernetes, Slurm)Python, shell scripting
Last updated Aug 7, 2026. Interested? Apply on the company site.

About NVIDIA

World Leader in AI Computing

NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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