Senior AI Infrastructure Platform Engineer (Cloud&Hardware)

Google; Kirkland (WA); On-site; USD 188,000 - 274,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid time off

Sick time

Maternity leave

Baby bonding leave

Holidays

Job summary

Google Cloud is seeking a Platform Application Engineer (Hardware Engineer) to drive deep hardware analysis and root-cause resolution for AI/ML infrastructure. You will work with engineering and product teams to improve platform solutions and deliver performant, reliable systems across on-prem and cloud environments.

Join a team focused on enabling customers to maximize their Google Cloud hardware investments, with opportunities to influence product direction and engineering practices at scale.

Qualifications

• 6 years of experience with technical infrastructure deployment, maintenance and troubleshooting.
• 6 years of debug or validation experience with CPU, dGPU, or TPU.
• 5 years of hardware debug across silicon, IO, memory or related areas.

Responsibilities

• Diagnose and resolve customer issues in AI/ML infrastructure through deep hardware analysis.
• Collaborate with Product, QA and Engineering to improve platforms and tools.
• Debug hardware and silicon issues to drive root-cause resolution.
• Understand AI/ML workloads and hardware architectures; build faster diagnosis tools.
• Serve as SME for internal stakeholders to resolve deployment obstacles.

Skills

Infrastructure debugging

Hardware debugging

Linux/Unix

System design

Troubleshooting cross-stack

Education

Bachelor's degree in CS/MIS or related

Tools

Kubernetes

Slurm

TensorFlow

PyTorch

Job description

Google Cloud is seeking a Platform Application Engineer (Hardware Engineer) to drive deep hardware analysis and root-cause resolution for AI/ML infrastructure. You will work with engineering and product teams to improve platform solutions and deliver performant, reliable systems across on-prem and cloud environments.

Join a team focused on enabling customers to maximize their Google Cloud hardware investments, with opportunities to influence product direction and engineering practices at scale.