Lead HPC & AI Infrastructure Architect - CA, San Jose, United States

Originally posted 13 October 2026 by the employer.

Architect large-scale CPU and GPU platforms across on-premises infrastructure, public cloud, and specialized GPU cloud providers.

About the role

As a Lead HPC & AI Infrastructure Architect, you will own the architecture and technical direction of large-scale CPU and GPU platforms across on-premises infrastructure, public cloud, and specialized GPU cloud providers. You will translate scientific computing, engineering, and AI requirements into secure, reliable, and cost-effective platforms for tightly coupled HPC workloads, distributed model training, and inference services.

What you'll do

• Own the HPC and AI infrastructure roadmap, reference architectures, and standards for CPU/GPU clusters; define workload placement across bare metal, virtual machines, Slurm, and Kubernetes.
• Lead designs covering CPU/NUMA and GPU topology, accelerator interconnects, networking, storage, and identity; establish validated hardware, firmware, driver, and runtime baselines.
• Architect RDMA-capable fabrics and storage tiers; validate collective communication, parallel I/O, dataset delivery, and checkpoint performance at target scale.
• Design resource sharing through Slurm accounting, fair-share, quotas, QoS, preemption, and tenant isolation; enable reproducible environments, self-service onboarding, and inference capacity planning.
• Standardize provisioning, configuration, and fleet upgrades through infrastructure as code, automation, and CI/CD; manage images, containers, and configuration drift.
• Benchmark HPC and distributed AI workloads; improve scaling efficiency, throughput, inference latency, GPU utilization, and cost efficiency through bottleneck analysis.

What you'll need

• 10+ years in infrastructure, cloud engineering, or distributed systems, including 5+ years designing and delivering production HPC or GPU platforms.
• Ownership of large-scale cluster deployments involving hundreds of nodes or comparable complexity, with measurable improvements in workload performance, reliability, or cost.
• Deep expertise in a major cloud platform such as Azure, OCI, or AWS, with experience integrating cloud and on-premises systems.
• Strong Linux troubleshooting skills across CPU/NUMA topology, PCIe, GPU connectivity, drivers, virtualization, containers, and distributed runtime environments.
• Hands-on Slurm scheduling and resource governance, plus Kubernetes experience. Understanding of MPI, distributed training, collective communication, and AI inference infrastructure.
• HPC networking and storage, including RDMA, RoCE or InfiniBand, topology and oversubscription tradeoffs, parallel I/O, data placement, checkpointing, and enterprise connectivity.

Nice to have

• Hands-on experience with AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads, including compatibility validation and performance troubleshooting.
• Experience with Azure, OCI, Crusoe, or TensorWave GPU environments, and cluster provisioning frameworks such as Azure CycleCloud or AWS ParallelCluster.
• Experience with Lustre, IBM Storage Scale, BeeGFS, or VAST, plus network, storage, and collective communication benchmarks used for cluster acceptance.
• Experience with LLM serving platforms such as vLLM or SGLang, model parallelism, inference capacity planning, and usage accounting or cost allocation.
• Understanding of rack power, liquid cooling, and facility capacity constraints, with experience coordinating platform requirements with hardware and data center teams.
• Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.

Skills: HPC & AI Infrastructure, CPU and GPU platforms, Slurm, Kubernetes, ROCm, PyTorch workloads