Software Engineer III, Diags Infrastructure

Build production and manufacturing diagnostic software that improves hardware uptime by reducing Mean Time To Repair (MTTR) and increasing useful hardware coverage.

Responsibilities

• Design, build, and scale next-generation diagnostic infrastructure for production and manufacturing environments
• Contribute to lowering MTTR and maximizing useful hardware coverage across global fleets
• Develop foundational software primitives and API capabilities to model diagnostic capabilities and validate test constraints
• Engineer diagnostic infrastructure, qualification frameworks, and dynamic distribution systems as part of the Diags Infrastructure team
• Create systems that describe, qualify, and distribute diagnostics across both test and production environments
• Drive automation for the repair ecosystem by delivering coverage-based recommendations to reduce operational intervention
• Support the execution of diagnostics by developing and providing critical infrastructure

Requirements

• Bachelor’s degree or equivalent practical experience
• 2 years of experience building and developing large-scale infrastructure and distributed systems
• 2 years of experience in programming
• 2 years of experience testing and launching software products
• 1 year of experience with distributed computing and large-scale data processing
• Master’s degree or PhD in Computer Science or related technical field
• 2 years of experience with data center architecture
• 2 years of experience with tools development
• 2 years of experience with monitoring systems
• 2 years of experience coding in Python and C++

Technologies

• Python
• C++

Location and Compensation

• Location: Sunnyvale, CA (onsite)
• Salary: USD 147,000 - 210,000 per year
• Compensation details: US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

About the team: Google’s Diags Infrastructure mission is to engineer diagnostic infrastructure, qualification frameworks, and dynamic distribution systems to minimize MTTR and maximize useful hardware coverage. The team uses coverage-based recommendations to automate parts of the repair ecosystem across both test and production environments.