AI Infrastructure Engineer

Amd

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
San Jose, CA
Salary
$204,000–$306,000 / yr
Posted
6 days ago
Freshness
Confirmed live yesterday
Closes
Sep 4, 2027

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $183k
This role $255k
$91k most similar roles pay here $329k

This role pays more than 91% of similar roles. Most pay $144,350–$221,100 — the shaded band above. At the midpoint, this role pays about $255k versus about $183k 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 367 open roles on FindRole.

Listed pay typically runs $166,400–$249,600 across 367 roles with salary data.

Most-posted roles

View all roles at Amd

At a glance

TL;DR · AI Infrastructure Engineer

As an AI Infrastructure Engineer, you will join the team building and operating large-scale GPU compute infrastructure to power AI and ML workloads. You will be responsible for building and extending platform capabilities for interactive development pods, CI pipelines, inference services, and benchmarking jobs. Your daily work involves designing and operating scalable orchestration systems using Kubernetes across on-prem and multi-cloud environments while developing features like secret management, configuration management, and deployment automation. You will manage service lifecycles using Helm and GitOps workflows such as ArgoCD or Flux, and integrate CSI drivers and network policies for high-performance workloads. Required skills include experience with Terraform, Prometheus, Grafana, and Loki. You will also work with machine learning frameworks like PyTorch, vLLM, and SGLang while leveraging expertise in HPC, Slurm, and GPU-based compute systems.

What you'll do

  • Build and extend platform capabilities to support new workloads like CI pipelines and inference services.
  • Design and operate scalable Kubernetes orchestration systems across on-premise and multi-cloud environments.
  • Develop platform features including secret management, configuration management, and deployment automation.
  • Partner with development teams to create APIs, templates, and self-service workflows for GPU platforms.
  • Manage service lifecycles within Kubernetes using Helm and GitOps tools like ArgoCD or Flux.
  • Integrate storage and networking components such as CSI drivers and network policies for high-performance workloads.

What we're looking for

  • Experience in DevOps, Platform, or Infrastructure Engineering.
  • Deep hands-on experience with Kubernetes and container orchestration at scale.
  • Proven ability to design and deliver platform features for internal customers or developer teams.
  • Experience building developer-facing platforms or internal developer portals.
  • Hands-on experience in storage or network engineering within Kubernetes environments.
  • Experience with Infrastructure as Code tools like Terraform.
  • Background in HPC, Slurm, or GPU-based compute systems for ML/AI workloads.
  • Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field.

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