Lead HPC & AI Infrastructure Architect

ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future. Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward.

Join us and, together, we’ll advance your career. THE ROLE We are seeking a Lead HPC & AI Infrastructure Architect to lead 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, scalable, and cost-effective platforms for tightly coupled HPC workloads, distributed model training, and inference services.

This hands-on leadership role spans design, workload validation, deployment, and lifecycle management. You will provide technical direction to infrastructure engineering and SRE teams and collaborate with researchers, AI engineers, security teams, cloud providers, hardware vendors, and data center teams to deliver production-ready platforms. THE PERSON You combine strong systems expertise with practical architectural judgment.

You approach complex technical problems collaboratively, validate decisions with measurable results, and communicate tradeoffs clearly. You work effectively across engineering, operations, security, and research teams; mentor engineers with varied backgrounds and experience levels; and balance performance, reliability, security, delivery timelines, and cost. You can lead initiatives with appropriate autonomy while engaging the partners needed to move from requirements through production acceptance.

KEY 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. PREFERRED EXPERIENCE Significant experience in infrastructure, cloud engineering, or distributed systems, including demonstrated experience designing and delivering production HPC or GPU platforms.

Equivalent practical experience is welcome. Experience leading large-scale cluster deployments involving hundreds of nodes, substantial multi-GPU environments, or systems of comparable technical complexity, with measurable improvements in workload performance, reliability, utilization, or cost. Experience with a ma