Customer Solutions Engineer, Compute, Google Cloud
Kirkland, WA
Full-time
Onsite
$102K/yr - $144K/yr
Entry Level
Google is seeking a Customer Solutions Engineer to support customers deploying AI and machine learning workloads on Google Cloud AI infrastructure. The role provides 24x7 specialized technical support through hardware and software debugging, networking, Linux administration, coding, and process improvement while collaborating with engineering, sales, and customer teams.
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
Qualification
Required
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
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
Health, dental, vision, life, disability insurance for all eligible US based employees
Retirement Benefits: 401(k) with company match for all eligible US based employees
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
