Hardware Development Engineer for AI/ML GPU Servers

Amazon Web Services (AWS); Cupertino (CA); On-site; USD 126,000 - 185,000; Full time

Job summary

Amazon Web Services (AWS) is seeking a Hardware Development Engineer in Cupertino, CA to join our GPU server platforms team. You will investigate hardware issues, analyze failure data, and work with senior engineers to understand server architecture, contributing to reliability and fleet operations.

You will learn end-to-end system behavior across hardware, firmware, software, and operations, with mentorship and opportunities to influence design improvements that power AI workloads for millions

Qualifications

• Bachelor's degree in electrical or computer engineering or equivalent.
• Hands-on Linux experience in coursework, labs or projects.
• Understanding of computer architecture (CPU, memory, buses, I/O).
• Ability to communicate technical concepts clearly and visually.
• Desire to work in a fast-paced, learning-intensive environment.

Responsibilities

• Investigate and root case hardware issues on GPU server platforms with guidance.
• Analyze failure data and trends to identify systemic problems and fixes.
• Work with senior engineers to understand server architecture, GPU subsystems, and fleet operations.
• Participate in design reviews for new server platforms with a reliability focus.
• Collaborate with cross-functional teams to close the loop between field failures and design improvements.
• Document findings, contribute to runbooks, and share knowledge with the team.

Skills

Python

Java

C

C++

Linux

Communication

Hardware debugging

Education

Bachelor's degree in Electrical or Computer Engineering

Tools

Linux

Job description

Amazon Web Services (AWS) is seeking a Hardware Development Engineer in Cupertino, CA to join our GPU server platforms team. You will investigate hardware issues, analyze failure data, and work with senior engineers to understand server architecture, contributing to reliability and fleet operations.

You will learn end-to-end system behavior across hardware, firmware, software, and operations, with mentorship and opportunities to influence design improvements that power AI workloads for millions