Job description

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 major cloud platform such as Azure, OCI, or AWS, including integration between cloud and on-premises systems. Familiarity with specialized GPU providers such as Crusoe or TensorWave is beneficial. • Strong Linux troubleshooting skills across CPU/NUMA topology, PCIe, GPU connectivity, drivers, virtualization, containers, and distributed runtime environments. • Hands-on experience with Slurm scheduling and resource governance, plus Kubernetes or comparable orchestration experience. Understanding of MPI, distributed training, collective communication, and AI inference infrastructure. • Experience with GPU computing platforms and distributed AI workloads. Experience with AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads, including compatibility validation and performance troubleshooting, is particularly relevant. • HPC networking and storage experience, including RDMA, RoCE or InfiniBand, topology and oversubscription tradeoffs, parallel I/O, data placement, checkpointing, and enterprise connectivity. • Experience with parallel and shared storage technologies such as Lustre, IBM Storage Scale, BeeGFS, NFS, VAST, or comparable technologies, as well as object storage and cluster acceptance benchmarking. • Experience with Terraform or equivalent infrastructure as code, Ansible or comparable configuration management, Python or Bash, and Git-based CI/CD. Familiarity with Azure CycleCloud, AWS ParallelCluster, or comparable cluster provisioning frameworks is beneficial. • Experience implementing observability and SRE practices using Prometheus, Grafana, or comparable technologies, including secure access, resource controls, fault recovery, and production readiness. • Ability to lead cross-functional technical work, clearly communicate architecture decisions and tradeoffs, and guide projects from requirements through production acceptance. • Experience coordinating with hardware and data center teams on rack power, liquid cooling, facility capacity, and other physical constraints affecting GPU platform design. • Preferred qualifications • Hands-on experience with AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads, including compatibility validation, benchmarking, and performance troubleshooting. • Experience with Azure, OCI, Crusoe, TensorWave, or comparable GPU environments and cluster provisioning frameworks such as Azure CycleCloud or AWS ParallelCluster. • Experience with Lustre, IBM Storage Scale, BeeGFS, VAST, or comparable high-performance storage platforms, 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. ACADEMIC CREDENTIALS • Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience. This role is not eligible for visa sponsorship. #LI-DP1 Benefits offered are described: AMD benefits at a glance . AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Requirements

Must-haves

ADVANCE YOUR CAREER. ADVANCE THE WORLD.
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.
Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
Benefits offered are described: AMD benefits at a glance .
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.

Nice-to-haves

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 with a major cloud platform such as Azure, OCI, or AWS, including integration between cloud and on-premises systems. Familiarity with specialized GPU providers such as Crusoe or TensorWave is beneficial.
Strong Linux troubleshooting skills across CPU/NUMA topology, PCIe, GPU connectivity, drivers, virtualization, containers, and distributed runtime environments.
HPC networking and storage experience, including RDMA, RoCE or InfiniBand, topology and oversubscription tradeoffs, parallel I/O, data placement, checkpointing, and enterprise connectivity.
Experience with parallel and shared storage technologies such as Lustre, IBM Storage Scale, BeeGFS, NFS, VAST, or comparable technologies, as well as object storage and cluster acceptance benchmarking.
Experience implementing observability and SRE practices using Prometheus, Grafana, or comparable technologies, including secure access, resource controls, fault recovery, and production readiness.
Ability to lead cross-functional technical work, clearly communicate architecture decisions and tradeoffs, and guide projects from requirements through production acceptance.
Experience coordinating with hardware and data center teams on rack power, liquid cooling, facility capacity, and other physical constraints affecting GPU platform design.
Hands-on experience with AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads, including compatibility validation, benchmarking, and performance troubleshooting.
Experience with Azure, OCI, Crusoe, TensorWave, or comparable GPU environments and cluster provisioning frameworks such as Azure CycleCloud or AWS ParallelCluster.
Experience with Lustre, IBM Storage Scale, BeeGFS, VAST, or comparable high-performance storage platforms, 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.

Application prep

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Keywords to mirror

aikubernetesci/cdazureawspythonllm

Platform: amd
Effort: medium

Benefits

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