Senior AI Accelerator & GPU Fleet Architect

Amazon Web Services (AWS); Cupertino (CA); On-site; USD 183,000 - 248,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Data Services, Inc. is seeking a Senior Hardware Development Engineer to own the GPU and accelerator lifecycle from definition to field operations. You will collaborate with vendor teams to shape hardware, firmware and diagnostic requirements and translate fleet-scale failure data into actionable improvements.

You will lead root-cause analyses, set health metrics, and present GPU lifecycle status to senior leadership while mentoring engineers in failure analysis methodologies.

Qualifications

• Bachelor's degree in electrical engineering, computer engineering, or equivalent.
• Experience in developing functional specifications, design verification plans and functional test procedures.
• 7+ years of hardware design and development experience for server, compute, or large-scale infrastructure platforms.

Responsibilities

• Own GPU component lifecycle strategy across AI accelerator platforms, including hardware, firmware and diagnostics requirements.
• Lead fleet-scale failure analysis using telemetry, event logs, and vendor diagnostics.
• Define GPU fleet health metrics, thresholds and dashboards for leadership review.
• Engage with vendors and cross-team engineering to translate failure data into action items and roadmaps.

Skills

Data analysis

Vendor engagement

Executive communication

Cross-team leadership

GPU/AI accelerator knowledge

Education

Bachelors in electrical/computer engineering

Tools

Firmware lifecycle tools

Telemetry / diagnostics tooling

Job description

Amazon Data Services, Inc. is seeking a Senior Hardware Development Engineer to own the GPU and accelerator lifecycle from definition to field operations. You will collaborate with vendor teams to shape hardware, firmware and diagnostic requirements and translate fleet-scale failure data into actionable improvements.

You will lead root-cause analyses, set health metrics, and present GPU lifecycle status to senior leadership while mentoring engineers in failure analysis methodologies.