Minimum qualifications:

• Bachelor’s degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
  
• 3 years of experience writing code in one or more general purpose programming languages (e.g., Java, C, C++, Python, Shell, Go or JavaScript, etc.) and in virtualization and orchestration frameworks.
• System administrator level experience with Linux/Unix systems.
• Experience debugging issues across the hardware/software boundary on enterprise-grade server infrastructure.
• 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).

Preferred qualifications:

• Experience working 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 working with large-scale distributed systems, and familiarity with common solutions, design patterns, or best practices.
  
• Familiarity with containerization and orchestration technologies like Kubernetes or Slurm in an on-prem or cloud environment.
  
• Excellent troubleshooting, attention to detail, and communication skills.

About the job:

As a Technical Solutions Engineer, you will be a trusted advisor to a range of customers. You'll look into complex technical challenges, troubleshoot critical issues across the Google Cloud platform, and provide solutions that help customers innovate with confidence. In this role, you will act as the advocate for the customer, collaborating with engineering and product teams to drive improvement in our products and services. You will be a part of a global team that provides support to ensure customers can seamlessly deploy their Artificial Intelligence (AI) and Machine Learning (ML) workloads on AI Infrastructure products. When customers encounter technical issues, you will 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 customer's tests related to AI deployments.

Responsibilities:

• Manage customer’s problems through effective diagnosis, resolution, or implementation of new investigation tools to increase productivity for customer issues on AI/ML infrastructure.
  
• Develop an 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 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.