Cloud AI Infrastructure Platform Engineer

Google LLC

Austin, Kirkland (TX, WA)

On-site

USD 159,000 - 230,000

Full time

7 days ago
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Benefits offered by this job

Health insurance
Dental insurance
Vision insurance
Life insurance
Disability insurance
401(k) with company match
PTO 20 days/year
Sick time 40 hours/year
Maternity leave 28-30 weeks
Holidays 13 days
Equity + bonuses

Job summary

Google LLC invites applications for a Share Platform Application Engineer in Cloud AI Infrastructure based in Austin, TX (or Kirkland, WA). You will investigate hardware observations, perform deep hardware analysis, and deliver long-term platform improvements across AI workloads.

The role emphasizes collaboration with engineering teams to drive product quality and scalable deployments, with a competitive compensation package.

Qualifications

  • Bachelor's degree in CS, MIS, or related technical field, or equivalent experience.
  • 4+ years debugging/validation with CPU, dGPU, or TPU.
  • 4+ years of infrastructure deployment, maintenance, and troubleshooting.
  • 3+ years hardware debugging (silicon/platform/IO/memory).
  • Experience debugging issues across hardware/software stack and with Linux/Unix.

Responsibilities

  • Manage AI/ML infrastructure problems through diagnosis, resolution, or tooling.
  • Collaborate with Product, Quality, and Engineering teams to improve products.
  • Debug platform hardware and silicon issues to drive root-cause resolution.
  • Understand AI/ML workloads and hardware architectures to aid faster diagnosis.
  • Act as SME for internal stakeholders to resolve deployment obstacles in AI infra.

Skills

Debugging
Linux/Unix
Platform debugging
AI/ML workloads
Kubernetes
Collaboration

Education

Bachelor's degree in Computer Science, MIS, or related field

Tools

TensorFlow
PyTorch
Slurm
Kubernetes

Job description

Google LLC invites applications for a Share Platform Application Engineer in Cloud AI Infrastructure based in Austin, TX (or Kirkland, WA). You will investigate hardware observations, perform deep hardware analysis, and deliver long-term platform improvements across AI workloads.

The role emphasizes collaboration with engineering teams to drive product quality and scalable deployments, with a competitive compensation package.

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