Customer Solutions Engineer, Compute, Google Cloud

We need to output HTML fragment with sections: Use

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content. We need to extract content. The posting includes: - Shift pattern/non-standard work hours: "This role requires you to work in a shift pattern or non-standard work hours as required. This may include weekend work." Also later: "Be available for non-standard work hours or shifts which may include weekends as needed." So schedule info: shift pattern, non-standard, weekends. - Benefits: listed: Health, dental, vision, life, disability insurance; Retirement Benefits: 401(k) with company match; Paid Time Off: 20 days vacation per year, accruing at 6.15 hours per pay period for first five years; 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. - Pay: "Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $102000 - $144000 (USD) + 15% bonus target + equity + benefits". So we have pay range. - Minimum qualifications: Bachelor's degree in STEM or equivalent practical experience. Experience in reading/debugging code in general purpose language (list) and in virtualization and orchestration frameworks. Experience troubleshooting and advocating for customer needs, triaging technical issues across stack. System administrator level experience with Linux/Unix systems and experience debugging issues across hardware/software boundary on enterprise-grade server infrastructure. - Preferred qualifications: Experience with large-scale distributed systems, familiarity with common solutions, design patterns, best practices. Experience working directly with AI/ML computing hardware, including GPUs or other accelerators. Experience with ML frameworks (TensorFlow, PyTorch), understanding of AI/ML training and inference lifecycle. Familiarity with containerization and orchestration technologies like Kubernetes or Slurm in on-prem or cloud environment. - About the Job: description: "Our Solutions Engineers for AI Infrastructure own complex customer issues and provide specialized support to other teams. In this role, you will be part of a global team that provides 24x7 support to ensure customers can seamlessly deploy their AI and ML workloads on AI Infrastructure products. When customers encounter deep technical issues, your job is to ensure we have the expertise, tools, and processes to resolve the issue. You will troubleshoot technical problems with a mix of hardware and software debugging, networking, Linux system administration, coding/scripting, and updating documentation. You will help our customer’s success in the AI/ML space by making improvements to the product, internal tools, processes, and documentation. You'll help drive business growth by recognizing and advocating for our customers’ challenges related to AI deployments. 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." - Responsibilities: list bullet points: 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. We also have "In most instances, this position requires in-person interviews as part of the hiring process." That's maybe metadata; we can fold into a short p or drop? It's not a section heading. Could be part of About the role? Probably not needed. We can include as a short p under maybe "About the role" or separate? The instruction: "Do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it." So we can include a short p for that sentence maybe under a generic "About the role"? But we have "About The Job" heading already. The "About The Job" section is present. So we can put that content under

About the role

(or About the Job?). The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should use those exact headings. So we need sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. But we only include sections if posting has real specific content. We have content for About the role (the paragraph about Solutions Engineers). Responsibilities (list). Requirements (minimum qualifications). Qualifications (preferred qualifications). Skills? Not explicitly separate; maybe we could combine skills under Requirements or Qualifications. But we could create a Skills section if there is explicit skill list. The posting includes many skill-like items but they are within qualifications. Probably we can omit Skills section if not distinct. Benefits: we have. Pay: we have. Schedule: we have shift pattern info. Thus we will output sections: About the role, Responsibilities, Requirements, Qualifications, Benefits, Pay, Schedule. We need to use

• for any list of duties/requirements/benefits. For prose, use

  . We must preserve all substantive information, not invent or summarize away detail. Must not include boilerplate like EEO, accommodation, etc. Remove those. We must not include headings with filler like "commensurate with experience". Not applicable. We need to ensure we include all specifics: e.g., benefits details exactly as given. Let's craft. First, About the role: combine the paragraph(s) from "About The Job". Also maybe include the sentence about in-person interviews? That's metadata; we could include as a short p under About the role? It's not about the role but hiring process. Could drop? Probably safe to drop as minor metadata. The instruction says fold minor metadata into a short

  or drop it. So we could include a short p: "In most instances, this position requires in-person interviews as part of the hiring process." That's minor metadata; we can include as a p under About the role. Now Responsibilities: list each bullet as

• . Requirements: minimum qualifications: list each as
• . Need to preserve details: 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. We'll put each as
• . Qualifications: preferred qualifications: list each as
• . Items: 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. Familiarity with containerization and orchestration technologies like Kubernetes or Slurm in an on-prem or cloud environment. Benefits: list each benefit as
• . Need to capture all details: 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. We could combine some items: maybe separate each as li. Pay: we need to include the pay range and bonus etc. Use

  maybe. Schedule: we need to include shift pattern, non-standard work hours, weekend work. Use

  or

  ? It's not a list but we can use

  . Now ensure we don't repeat job title as heading. Not needed. Now output HTML fragment only. Let's construct:

  About the role

  Our Solutions Engineers for AI Infrastructure own complex customer issues and provide specialized support to other teams. In this role, you will be part of a global team that provides 24x7 support to ensure customers can seamlessly deploy their AI and ML workloads on AI Infrastructure products. When customers encounter deep technical issues, your job is to ensure we have the expertise, tools, and processes to resolve the issue. You will troubleshoot technical problems with a mix of hardware and software debugging, networking, Linux system administration, coding/scripting, and updating documentation. You will help our customer’s success in the AI/ML space by making improvements to the product, internal tools, processes, and documentation. You'll help drive business growth by recognizing and advocating for our customers’ challenges related to AI deployments. 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.

  In most instances, this position requires in-person interviews as part of the hiring process.

  Responsibilities

  • 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.

  Requirements

  • 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.