Senior Solutions Architect, Generative AI

Role Description

NVIDIA is looking for an AI Solutions Architect 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:

• Accelerating customer workloads.
• Designing high-performance AI infrastructure.
• Leading technical engagements around NVIDIA technologies.
• 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 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.

Requirements

• 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

• Competitive salaries.
• Generous benefits package.
• Equity eligibility.

Company Description

NVIDIA is recognized as one of the technology world’s most sought-after employers. This role offers a chance to make a broad impact at NVIDIA by advancing innovation with our consumer internet & frontier labs partners. We are committed to fostering an inclusive work environment and proud to be an equal opportunity employer.