US: $102000 - $144000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Manage customers' problems through effective diagnosis, resolution, or implementation of new investigation tools to increase productivity for customer issues on AI/ML infrastructure.
Develop an in-depth understanding of AI/ML workloads and underlying hardware architectures by troubleshooting, reproducing, determining the root cause for customer reported issues, and building tools for faster diagnosis.
Act as a consultant and subject matter expert for internal stakeholders in Engineering, Sales, and customer organizations to resolve complex deployment and operational obstacles in AI infrastructure environments.
Work closely with multiple Product and Engineering teams to find ways to improve the product, and interact with our Site Reliability Engineering (SRE) teams to drive high-quality production.
Be available for non-standard work hours or shifts which may include weekends as needed.
Minimum qualifications
Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
Experience in reading/debugging code written in a general purpose coding language (e.g., Java, C, C++, Python, Shell, Go or JavaScript, etc.) and in virtualization and orchestration frameworks.
Experience troubleshooting and advocating for customer needs, and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, performance).
System administrator level experience with Linux/Unix systems and experience in debugging issues across the hardware/software boundary on enterprise-grade server infrastructure.
Preferred qualifications
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 ML frameworks (e.g., TensorFlow, PyTorch), and understanding of the AI/ML training and inference lifecycle.
Experience with containerization and orchestration technologies like Kubernetes or Slurm in an on-prem or cloud environment.
Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
Experience in reading/debugging code written in a general purpose coding language (e.g., Java, C, C++, Python, Shell, Go or JavaScript, etc.) and in virtualization and orchestration frameworks.
Experience troubleshooting and advocating for customer needs, and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, performance).
System administrator level experience with Linux/Unix systems and experience in debugging issues across the hardware/software boundary on enterprise-grade server infrastructure.
