Enable massive scale so that ML engineers working primarily in Python can iterate locally and seamlessly scale to 1,000,000+ accelerators without needing to become experts in infrastructure. Act as a proxy for the emerging AI/ML end-user persona, evolving the GKE team's intuition and empathy for real-world problems and opportunities within the AI workload lifecycle. Drive the development and architectural outlook of a substrate optimized for Accelerated and Agentic Workloads. Imagine, architect, and lead the technical execution of industry-defining standards through both direct, direct technical work and by mentoring and guiding teams of engineers.
Job description provided by Google.
View the original posting
