ML Fleet Hardware Engineer

Amazon Web Services (AWS); Austin (TX); On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Annapurna Labs (U.S.) Inc. is seeking a Platform Development Engineer to own an ML server platform within our fleet. You will maximize health, sellability, and customer experience while collaborating with hardware and software teams.

You will lead end-to-end testing, drive automation, and develop data infrastructure to scale operations, with dashboards guiding triage and incident response across globally deployed ML servers.

Qualifications

• 2+ years of non-internship software development experience
• 1+ years of designing or architecting scalable systems
• 1+ years of admin experience in networking, storage, OS, and hands-on systems engineering
• Knowledge of systems engineering fundamentals (networking, storage, OS)
• Experience programming with C++, C#, Java, Python, Golang, PowerShell, Ruby
• Experience with Linux/Unix
• Experience debugging and systems analysis to identify and resolve issues
• Bachelor's degree in CS/CE/EE

Responsibilities

• Be part of a team responsible for system remediation and customer experience on ML products
• Use data to root cause hardware failures and identify trends on AWS’s complex systems
• Implement and improve system level testing across the product lifecycle
• Develop maintainable, well-documented, tested software to scale operations
• Investigate issues at the intersection of hardware and software

Skills

Software development

C++

Python

Java

Linux/Unix

Systems engineering

Debugging

Education

Bachelor's degree in CS/CE/EE

Master's degree in CS/CE/EE

Job description

Annapurna Labs (U.S.) Inc. is seeking a Platform Development Engineer to own an ML server platform within our fleet. You will maximize health, sellability, and customer experience while collaborating with hardware and software teams.

You will lead end-to-end testing, drive automation, and develop data infrastructure to scale operations, with dashboards guiding triage and incident response across globally deployed ML servers.