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Thu, January 1AI-NativePlatform Engineering & DevOps
Google's source, build, test, continuous integration, and release systems are under unprecedented pressure as software development scales from tens of thousands of human developers to millions of machine-speed developers. Many of the assumptions that guided the design of developer infrastructure no longer hold. Unlike human developers, machine developers can be created on demand, fundamentally changing the scale and behaviour of engineering workloads.
This session examines how these changes are reshaping the systems that support software development. Drawing on Google's experience, we will explore the techniques used to ensure that source, build, and test infrastructure continues to scale as development patterns evolve. The discussion covers how engineering teams identify the systems that require optimization, monitor for layered bottlenecks, determine which parts of the development pipeline experience the greatest pressure, and apply optimization strategies as workloads continue to grow.
The session also explores the role of AI automation in identifying optimization opportunities and the safeguards used to preserve the reliability and safety guarantees expected from developer infrastructure. Rather than focusing on individual development tools, the talk examines the engineering challenges involved in operating the systems that support software development at machine scale.
What You Will Learn:
Who Should Attend:
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Daniel serves as a Principal Engineer for Google's Scaled Software Enablement organization, which tackles the critical problem of how to effectively build and scale our developer systems for humans and agents, and serves as the global co-chair of Google's SWE Steering Committee. At Google prior to this Daniel served as a principal with the office of Cross Google Engineering, tackling cross-company technical strategy, and has led cross-functional teams across the software stack including Google’s geographic data infrastructure, Google My Business Locations, Google Photos, and Google Tasks among others. His experience traverses the technical spectrum and includes infrastructure, machine learning, mobile and web.
