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Lead HPC & AI Infrastructure Architect

AMD· Engineering
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San Jose, CA$210.4kFull-timePosted 1w ago

About the role

Lead the architecture and technical direction of large-scale CPU and GPU platforms across on-premises infrastructure, public cloud, and specialized GPU cloud providers to support tightly coupled HPC workloads, distributed model training, and AI inference services.

Responsibilities

  • Lead 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.
  • Define production acceptance criteria, service-level objectives, and fleet observability. Partner with SRE teams on fault isolation, job recovery, checkpoint/restart, incident analysis, and capacity planning.
  • Benchmark HPC and distributed AI workloads; improve scaling efficiency, throughput, inference latency, GPU utilization, reliability, and cost efficiency through bottleneck analysis.
  • Incorporate federated identity, least-privilege access, tenant and network isolation, secrets management, controlled container privileges, audit logging, and vulnerability management into platform designs.
  • Lead architecture reviews, proofs of concept, provider evaluations, and complex technical problem solving. Document decisions and tradeoffs, mentor engineers, and align delivery with stakeholders and infrastructure partners.

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.

Skills

Required:Infrastructure architectureLinux troubleshootingSlurm scheduling and resource governanceKubernetes or orchestrationMPI, distributed training, collective communication
Last updated Oct 22, 2026. Interested? Apply on the company site.
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