Director, Engineering, Compute Infrastructure, Google Distributed Cloud

Google Distributed Cloud (GDC) is a cloud-centric platform that enables enterprises to run modern apps anywhere consistently at scale. We offer a wide spectrum of solutions from managed software on your own hardware, fully managed hardware and AI-led software services, to completely air-gapped sovereign offerings.

As Director of Engineering for Compute Infrastructure in GDC Services, you will lead a foundational engineering organization responsible for the core compute infrastructure that power Google Distributed Cloud.

This role gives you direct ownership of:

• The bedrock for sovereign AI: Your compute stack provides the high performance, confidential, and resilient substrate upon which Google’s flagship services, including on-prem Gemini, Vertex AI, autonomous agentic runtimes, are built, scheduled, and executed at scale.

• A flagship customer facing surface: Your organization owns the end to end compute infrastructure surface (APIs, CLI, UI, workload lifecycle, and multi-tenancy) that sovereign customers interact with daily to operate their most mission critical VM fleets.

As the Director of Engineering for Compute Infrastructure in Google Distributed Cloud, you will define the multi year technical vision and product execution roadmap across connected, edge, and fully disconnected airgapped topologies. Partnering with Product Management, SRE, Systems/Hardware Engineering, Security, and Google DeepMind/Cloud AI, you will establish a robust and resilient compute foundation engineered for the highest security boundaries in the world (FedRAMP, IL5, ITAR, Sovereign Controls).

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $307000 - $427000 (USD) + 30% bonus target + equity + benefits

Learn more about benefits at Google . Responsibilities

• Unite modern cloud native applications with next-generation virtualization on a single, declarative control plane. Solve one of the greatest infrastructure transitions in enterprise IT, giving customers a modern alternative to legacy virtualization stacks without requiring them to rewrite decades of enterprise VM applications.

• Partner closely with GDC AI, Vertex AI, and DeepMind teams to optimize bare metal and virtualized compute substrates for demanding AI inference and agentic workflows. Drive hardware and GPU utilization, NUMA aware scheduling, SR-IOV, GPU passthrough, and confidential computing to unlock maximum price-performance and zero-trust security.

• Design systems for environments where central public cloud SREs cannot manually intervene.

Minimum qualifications:

• Bachelor's degree in Computer Science or Engineering, or equivalent practical experience.

• 15 years of experience as a software engineering leader in infrastructure areas.

• 10 years of experience leading multiple engineering teams and senior technical leaders (managers and principal/staff engineers).

• Experience with cross-functional senior stakeholder management.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.

• Experience with cloud services in the areas of Compute, Virtual Machines, Storage, or Networking.

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Proven track record of designing and shipping successful product experiences for web and mobile interfaces.

• Proven track records of designing, shipping, and operating packaged, edge, hybrid cloud, or airgapped/disconnected infrastructure for enterprise or highly regulated public-sector customers (e.g., FedRAMP, Sovereign Data Centers).

• Proven track record optimizing compute, Virtual Machines for AI/ML workloads (GPU/accelerator scheduling, SR-IOV, NUMA topology, confidential computing).

• Active technical influence and community leadership within open source ecosystems.