below marketmost similar roles pay hereabove market
This role pays more than
83%
of similar roles. Most pay
$180,262–$243,950
— the blue band above.
At the midpoint, this role pays about
$263k
versus about
$212k
for comparable roles.
Based on 240 similar postings.
Employer
About Amd
AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors
Amd currently has
496 open roles
on FindRole.
Listed pay typically runs
$172,000–$258,000
across 496 roles with salary data.
The Lead HPC & AI Infrastructure Architect leads the architecture and technical direction of large-scale CPU and GPU platforms across on-premises infrastructure, public cloud, and specialized GPU cloud providers. This role involves translating scientific computing and AI requirements into secure, scalable platforms for tightly coupled HPC workloads, distributed model training, and inference services. Key responsibilities include defining roadmap standards for CPU/GPU clusters, designing RDMA-capable fabrics, and managing resource sharing through Slurm and Kubernetes. The architect will utilize technologies including AMD Instinct GPUs, ROCm, RCCL, PyTorch, Terraform, Ansible, and Prometheus. Core competencies involve managing complex systems like Lustre or BeeGFS storage, optimizing collective communication, and ensuring production readiness through CI/CD automation. The role solves technical challenges regarding workload placement, performance benchmarking, and hardware-software integration for high-performance computing.
Lead the HPC and AI infrastructure roadmap, reference architectures, and standards for CPU/GPU clusters.
Design CPU/NUMA and GPU topologies, accelerator interconnects, networking, storage, and identity systems.
Architect RDMA-capable fabrics and storage tiers to validate collective communication and parallel I/O performance.
Design resource sharing through Slurm accounting, fair-share, quotas, and tenant isolation for reproducible environments.
Standardize provisioning and fleet upgrades using infrastructure as code, automation, and CI/CD pipelines.
Define production acceptance criteria, service-level objectives, and fleet observability in partnership with SRE teams.
Benchmark distributed AI workloads to improve scaling efficiency, throughput, inference latency, and GPU utilization.
Integrate federated identity, least-privilege access, and vulnerability management into all platform designs.
What we're looking for
Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
Significant experience in infrastructure, cloud engineering, or distributed systems, including designing and delivering production HPC or GPU platforms (preferred).
Experience leading large-scale cluster deployments involving hundreds of nodes or substantial multi-GPU environments (preferred).
Experience with major cloud platforms like Azure, OCI, or AWS, including integration between cloud and on-premises systems (preferred).
Strong Linux troubleshooting skills across CPU/NUMA topology, PCIe, GPU connectivity, drivers, virtualization, containers, and distributed runtime environments (preferred).
Hands-on experience with Slurm scheduling, Kubernetes, MPI, distributed training, and AI inference infrastructure (preferred).
Experience with GPU computing platforms, including AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads (preferred).
Experience with HPC networking (RDMA, RoCE, InfiniBand) and parallel storage technologies like Lustre, IBM Storage Scale, or BeeGFS (preferred).
Kubernetes
Go
Python
Linux
AWS
EKS
Terraform
Argo CD
Helm
Prometheus
Grafana
PyTorch
Ray
vLLM
SGLang
TensorRT-LLM
CUDA
NVLink
NVSwitch
NCCL
EFA
DCGM
Containers
HPC