Job Description

Minimum qualifications

• Bachelor’s degree in Computer Science, Electrical Engineering, or equivalent practical experience
• 15 years of experience in software development in software design, data structures, algorithms, debugging, and analysis on working on customer-facing products
• 10 years of experience working with private and public cloud design considerations and challenges in the areas of operating systems, virtualization, multi-tenancy, fault-isolation, large-scale distributed systems, networking, and security, with technical paper publications and conference engagements
• 10 years of experience in cloud compute infrastructure and storage or networking.

Preferred qualifications

• Experience successfully building software and large-scale distributed systems
• Ability to work cross-functionally, partnering with groups (e.g., Engineering, Product Management, Finance, Account Management, Operations, Product Marketing, UX, and UI), brokering trade offs with stakeholders, understanding their needs, and driving consensus
• Excellent organization and prioritization skills along with outstanding written and verbal communication skills
• Outstanding narrative and storytelling skills that propel usage, adoption, and market momentum

About the job

As the Principle Engineer for Specialized Instances, you will establish workload-optimized portfolio roadmap by defining GCE instances that meet the stringent resource, reliability, and performance demands of large-scale AI/ML clusters and mission-critical

applications. These instances are in the category of Storage, Networking, and Memory Optimized, as well as HPC.

Our workload-optimized portfolio also includes memory and storage-optimized instances. Storage-optimized instances are ideal for data-intensive applications like high-performance databases (e.g., SAP HANA), data warehousing, and large-scale media processing, where rapid access to vast datasets is essential. Memory-optimized instances exceed at running in-memory databases (e.g. Redis) and real-time analytics platforms (e.g. Apache Spark).

In this role, you'll also manage the challenge of providing low-latency, high-throughput storage solutions essential for the exponential growth of AI/ML clusters. This includes optimizing for applications that require rapid access to training data, checkpointing, and model serving, where local SSDs and storage-optimized VMs are critical for achieving optimal performance.

Additionally, you will drive technical innovation to push the boundaries of performance, reliability, and cost-efficiency across all these workloads, ensuring our customers have a seamless and exceptional experience. This is an opportunity to shape the future of cloud computing by directly impacting the core compute offerings of Google Cloud Platform.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

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

• Establish industry-leading workload-optimized portfolio roadmap by defining GCE instances that meet the stringent resource, reliability, and performance demands of large-scale AI/ML clusters and mission-critical applications.
• Lead the design, development, and optimization of GCE instances tailored to the unique performance and reliability needs of unique workloads, including collaborating with internal teams and Google’s hardware architects to ensure optimal performance and efficiency.
• Build understanding of enterprise and AI/ML segments as a subject matter expert to understand their current and future requirements for GCE instances.
• Provide technical guidance and mentorship to tech leads responsible for various aspects of the specialized GCE instance portfolio.