Cloud AI Systems Engineer - Generative AI & ML Servers

Amazon Web Services (AWS); Cupertino (CA); On-site; USD 149,000 - 201,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Job summary

The Hardware Engineering AI/ML development team at Amazon Data Services, Inc. Cupertino is seeking a technically adept leader to design and deliver next-generation AWS platforms.

You will own significant parts of the server stack, from hardware to userland, and guide cross-team collaboration to improve reliability and performance for AI/ML workloads. The role emphasizes solving complex architectural problems, decomposing large server-system challenges, and delivering scalable solutions.

Qualifications

• 2+ years of non-internship professional software development experience.
• 1+ years designing or architecting (design patterns, reliability and scaling) of new and existing systems experience.
• 7+ years of administrative experience in networking, storage systems, operating systems and hands-on systems engineering experience.
• Knowledge of systems engineering fundamentals (networking, storage, operating systems).
• Experience programming with at least one modern language such as C++, C#, Java, Python, Golang, PowerShell, Ruby.

Responsibilities

• Lead complex architectural problems and own the team systems.
• Decompose large server-system testability, reliability and diagnosis problems into manageable tasks.
• Collaborate with AWS Hardware Eng, EC2, and other AWS services to scale solutions.
• Write tactical code to diagnose issues and prevent customer impact.

Skills

Software development

C++/Java

Linux fundamentals

Tools

PowerShell

Python

Ruby

C#

Job description

The Hardware Engineering AI/ML development team at Amazon Data Services, Inc. Cupertino is seeking a technically adept leader to design and deliver next-generation AWS platforms.

You will own significant parts of the server stack, from hardware to userland, and guide cross-team collaboration to improve reliability and performance for AI/ML workloads. The role emphasizes solving complex architectural problems, decomposing large server-system challenges, and delivering scalable solutions.