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Thu, April 29Platform Engineering & DevOpsAI-Assisted Software EngineeringReliability, Observability & Security
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. Unlike human developers, machine developers can be created on demand, challenging many of the assumptions that shaped traditional developer infrastructure.
In this session, you will explore how these changes are reshaping the systems that support software development. Drawing on Google’s experience, you will examine how engineering teams identify systems that require optimisation, monitor for layered bottlenecks, determine which parts of the development pipeline are under the greatest pressure, and apply optimisation strategies as workloads grow. You will also explore how AI automation can identify optimisation opportunities and how engineering safeguards help preserve the reliability and safety guarantees expected from developer infrastructure.
What You Will Learn
How developer infrastructure changes as software development scales to machine-speed workloads
How to identify bottlenecks, monitor critical systems, and prioritise infrastructure optimisation
How AI automation and engineering safeguards contribute to scalable, reliable developer infrastructure
Who Should Attend
Platform Engineers, Developer Infrastructure Engineers, Build and Release Engineers, DevOps Engineers, Software Architects, Staff and Principal Engineers, and Technical Leads responsible for engineering platforms.
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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.
