Minimum qualifications:- 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.
Preferred qualifications:- 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 with coding in Python and C .
About the jobOur team's mission is to engineer diagnostic infrastructure, qualification frameworks, and dynamic distribution systems to minimize Mean Time To Repair (MTTR) and maximize useful hardware coverage. We drive automation by providing coverage-based recommendations to the repair ecosystem, minimizing operational intervention across both test and production environments.
As a member of the Diags Infrastructure team, you will develop and provide critical infrastructure for the execution of diagnostics. Your primary objectives will be achieved by collaborating closely with stakeholders and the wider diagnostics team.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities- Design, build, and scale our next-generation diagnostic infrastructure for production and manufacturing environments.
- Play a critical role in minimizing MTTR and maximizing useful hardware coverage across global fleets.
- Implement foundational software primitives and API capabilities to model diagnostic capabilities and validate test constraints.
- Join the Diags Infra team to engineer diagnostic infrastructure, qualification frameworks, and dynamic distribution systems.
- Focus on building systems to describe, qualify, and distribute diagnostics across both test and production environments. Drive automation by providing coverage-based recommendations to the repair ecosystem, minimizing operational intervention.
