Senior Solutions Architect, Generative AI

• 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.

What We Need To See

• 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.

Ways To Stand Out From The Crowd

• 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.
• We make extensive use of conferencing tools, but occasional travel (20%) is required for local on-site visits to customers and conferences.
• We are open to remote work.
• We look forward to having you join our team!
• With competitive salaries and a generous benefits package, 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.
• Are you inventive, diligent, committed, and driven?
• Do you enjoy tackling challenges?
• If so, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 3, 2026.