Platform Application Engineer, Cloud AI Infrastructure
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- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
- Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
- Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
Minimum qualifications:
- Bachelor's degree in Computer Science, Management Information Systems, or other technical field, or equivalent practical experience.
- 4 years of debug or validation experience with CPU, dGPU, or TPU.
- 4 years of experience with technical infrastructure (deployment, maintenance, and troubleshooting), and quality and reliability of technical infrastructure.
- 3 years of experience with hardware debug (silicon debug, platform debug, IO interface, or memory analysis).
- Experience debugging technical issues across the stack (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.
Preferred qualifications:
- Experience working 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.
- Familiarity with containerization and orchestration technologies like Kubernetes or Slurm in an on-prem or cloud environment.
- Experience with systems automation, and with systems design and debug.
- Experience with ML frameworks (e.g., TensorFlow, PyTorch), and understanding of the AI/ML training and inference lifecycle.
- Advanced understanding of memory and high-speed input/output (IO) technologies.
About the job
Our AI Infrastructure Engineering Support team is dedicated to ensuring our customers get the most out of their Google Cloud hardware investment. As a Platform Application Engineer (Hardware Engineer), you will be focused on solving customer observations by, driving deep hardware analysis, debug, and issue resolution through to root cause. You will dive deep into complex technical challenges, troubleshoot critical issues across the platform, and provide resolutions in both short-term and long-term platform solutions. In this role, you will represent the customer solution, collaborating tightly with engineering and product teams to drive continuous improvement in our products and services.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $159000 - $230000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Manage customer’s problems through effective diagnosis, resolution, or implementation of new investigation tools to increase productivity on AI/ML infrastructure.
- Work closely with multiple Product, Quality, and Engineering teams to improve the product, and interact with our Site Reliability Engineering (SRE) teams to understand behaviours.
- Debug platform hardware and silicon-related issues to drive root-cause resolution and develop permanent improvements.
- Drive understanding of AI/ML workloads and underlying hardware architectures by troubleshooting, reproducing, determining the cause for customer reported issues, and building tools for faster diagnosis.
- Act as a consultant and subject matter expert for internal stakeholders in Engineering and Quality organizations to resolve complex deployment and operational obstacles in AI infrastructure environments.
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