Senior Platform Application Engineer, Cloud AI Infrastructure

Google•Kirkland, WA

1d•$188,000 - $274,000

About The Position

The Senior Platform Application Engineer will support customers using Google Cloud hardware by investigating customer observations and resolving complex technical issues. The role focuses on deep hardware analysis, debugging, and root-cause resolution across the platform, with short- and long-term solutions. The engineer represents customer needs and collaborates closely with engineering, product, quality, and Site Reliability Engineering teams to improve products and services. The position also involves developing tools that make diagnosis of AI/ML infrastructure issues faster and more effective.

Requirements

Bachelor's degree in Computer Science, Management Information Systems, or another technical field, or equivalent practical experience.
6 years of experience with technical infrastructure deployment, maintenance, troubleshooting, and infrastructure quality and reliability.
6 years of CPU, discrete GPU, or TPU debugging or validation experience.
5 years of hardware debugging experience, such as silicon debug, platform debug, I/O interface, or memory analysis.
Experience debugging technical issues across the stack, including hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, or performance.
Experience with Linux/Unix systems and debugging issues across the hardware/software boundary on enterprise-grade server infrastructure.

Nice To Haves

Experience working with large-scale distributed systems and familiarity with common solutions, design patterns, or best practices.
Experience working directly with AI/ML computing hardware, including GPUs or other accelerators.
Experience with systems automation, systems design, and debugging.
Experience with ML frameworks such as TensorFlow or PyTorch, and understanding of the AI/ML training and inference lifecycle.
Familiarity with containerization and orchestration technologies such as Kubernetes or Slurm in an on-premises or cloud environment.
Understanding of memory and high-speed I/O technologies.

Responsibilities

Manage customer problems through diagnosis, resolution, or implementation of investigation tools to improve productivity on AI/ML infrastructure.
Work with Product, Quality, and Engineering teams to improve products, and interact with Site Reliability Engineering teams to understand system behaviors.
Debug platform hardware and silicon-related issues, establish root causes, and develop permanent improvements.
Build understanding of AI/ML workloads and underlying hardware architectures by troubleshooting and reproducing customer-reported issues, determining causes, and developing faster diagnostic tools.
Act as a consultant and subject matter expert for Engineering and Quality stakeholders resolving complex deployment and operational obstacles in AI infrastructure environments.