Senior HPC & AI Infrastructure Architect

Advanced Micro Devices, Inc.; San Jose (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Benefits at a glance

Job summary

Advanced Micro Devices, Inc. seeks a Lead HPC & AI Infrastructure Architect to own architecture and direction of large-scale CPU and GPU platforms across on‑prem, cloud, and GPU cloud providers.

You will translate scientific computing and AI needs into secure, reliable, and cost-effective platforms for HPC, training, and inference. This hands‑on leadership role spans design, validation, deployment, and lifecycle management.

Qualifications

• Bachelor’s degree in CS/CE or related field; equivalent practical experience accepted.
• Strong Linux troubleshooting and HPC cluster domain knowledge.
• Hands-on Slurm and Kubernetes experience, MPI and distributed training concepts.

Responsibilities

• Own HPC/AI infrastructure roadmap, reference architectures, and standards.
• Architect CPU/GPU clusters across on‑prem, cloud, and GPU cloud providers.
• Lead designs for topology, interconnects, storage, and networks; establish baselines.
• Define production acceptance criteria, SLIs/SLOs, and fleet observability.
• Mentor engineers, drive IaC, CI/CD, and guard against configuration drift.
• Partner with SRE, researchers, cloud providers, and hardware vendors.

Skills

Linux troubleshooting

Slurm scheduling

Kubernetes

MPI

Distributed training

AI inference infrastructure

Infrastructure as code

Terraform

Ansible

Python

Bash

Git CI/CD

Prometheus

Grafana

Cross-functional leadership

Mentoring

Education

Bachelor's degree in Computer Science, Computer Engineering, or related field

Tools

Azure CycleCloud

AWS ParallelCluster

Lustre

IBM Storage Scale

BeeGFS

VAST

Prometheus

Grafana

Job description

Advanced Micro Devices, Inc. seeks a Lead HPC & AI Infrastructure Architect to own architecture and direction of large-scale CPU and GPU platforms across on‑prem, cloud, and GPU cloud providers.

You will translate scientific computing and AI needs into secure, reliable, and cost-effective platforms for HPC, training, and inference. This hands‑on leadership role spans design, validation, deployment, and lifecycle management.