Software Engineer III, Reliability Infrastructure, Workspace Platform
Confirmed on the employer's own hiring board on Oct 22, 2026. First seen by Alion on Oct 21, 2026. Google scores B on the Alion truth index.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Our mission is to ensure Workspace resilience by providing zero-touch, automated infrastructure for continuous data recovery validation and proactive dependency risk assessment.
We proactively identify and mitigate risks by building infrastructure for automated data recovery testing, fault-injection pipelines, and dependency detection systems that give engineers actionable insights into product stability.
Simulating users across various operational scales, our infrastructure is expected to model the various customer landscape of Workspace. Our team maintains ongoing, close collaboration with first-party application teams and SREs to cultivate unified platform capabilities across the entire Workspace suite.
AI will change the future of work in profound ways, and our products- Gmail, Docs, Drive, Calendar, Sheets, Vids and Meet are at the forefront. From pre-computed summaries for email threads, summaries for meetings, and videos created from a document using lifelike AI avatars, our AI opportunity is huge. Our mission is to meaningfully connect people so they can create, build, and grow together and as part of the team you can build how productivity tools should work 5-10 years into the future. You will work with model builders (Google DeepMind), work with exceptional leaders, and have the ability to impact billions of users across the world.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
- Write product or system development code.
- Build internal software products to validate, measure and report Time to Recovery (TTR) end-to-end for various data restore scenarios on an ongoing basis.
- Lead and manage large-scale data seeding pipelines to support realistic disaster recovery testing and performance validation across Workspace applications.
- Ensure all work upholds high standards of engineering excellence, including comprehensive test coverage, clear documentation, effective monitoring, and strong debuggability.
- Provide automated solutions to ensure Workspace apps remain resilient to dependency failures by accurately classifying hard vs soft dependencies and understand and translate stakeholder requirements into robust software solutions.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages (e.g., Java and C++).
2 years of experience building and managing distributed software systems and infrastructure.
- 2 years of experience testing, launching software products, and building developer tools that improve developer velocity, code quality and code health (e.g., compilers, automated releases, code design and testing, test automation frameworks).
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures and algorithms in either an academic or industry setting.
- 2 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
- 2 years of Chaos engineering, using fault injection frameworks to simulate failure scenarios.
- 2 years of experience with synthetic data generation, managing pipelines for processing or transferring large datasets.