Platform Application Engineer, Cloud AI Infrastructure

Google•Kirkland, WA

13h•$159,000 - $230,000

About The Position

The Platform Application Engineer (Hardware Engineer) works on Google Cloud’s AI Infrastructure Engineering Support team to help customers get the most from their Google Cloud hardware investment. The role focuses on investigating customer observations through deep hardware analysis, debugging, and issue resolution through root cause. It involves troubleshooting complex technical challenges and critical platform issues, then providing short- and long-term solutions. The engineer represents customer needs and collaborates closely with engineering, product, quality, and Site Reliability Engineering teams to improve Google Cloud products and services. The role also involves building tools to speed diagnosis and serving as a consultant and subject matter expert on complex deployment and operational obstacles in AI infrastructure environments.

Requirements

Bachelor’s degree in Computer Science, Management Information Systems, or another technical field, or equivalent practical experience.
At least 4 years of debug or validation experience with CPU, dGPU, or TPU.
At least 4 years of experience with technical infrastructure deployment, maintenance, and troubleshooting, and with infrastructure quality and reliability.
At least 3 years of experience with hardware debug, such as silicon debug, platform debug, IO 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 across the hardware/software boundary on enterprise-grade server infrastructure.

Nice To Haves

Experience with 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.
Advanced understanding of memory and high-speed input/output (IO) technologies.

Responsibilities

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