Field Application Engineer, Cloud AI Infrastructure

Responsibilities

• Participate in on-call activities and manage server and data center CPU- and TPU-based activities, working with primary responders to resolve customer system observations.
• Manage customers' problems through effective diagnosis, resolution, or implementation of new investigation tools to increase productivity on AI/ML infrastructure.
• Work closely with Product, Quality, and Engineering teams to improve the product. Interact with our Site Reliability Engineering (SRE) teams to drive high-quality attainment.
• 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.
• - Participate in on-call activities and manage server and data center CPU- and TPU-based activities, working with primary responders to resolve customer system observations. - Manage customers' problems through effective diagnosis, resolution, or implementation of new investigation tools to increase productivity on AI/ML infrastructure. - Work closely with Product, Quality, and Engineering teams to improve the product. Interact with our Site Reliability Engineering (SRE) teams to drive high-quality attainment. - 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.

Minimum qualifications:

Preferred qualifications:

Qualifications

• Minimum qualifications: - Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience. - 4 years of experience in debug or validation with CPU, dGPU, or TPU. - 3 years of experience with technical infrastructure (deployment or maintenance, and troubleshooting), and with quality and reliability of technical infrastructure. - 3 years of experience with hardware debug (e.g., silicon, platform, IO interface, or memory analysis). - Experience with Linux/Unix systems and debugging issues across hardware/software boundary on enterprise-grade server infrastructure. - Experience troubleshooting and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, or virtualization). Preferred qualifications: - Experience working directly with AI/ML computing hardware, including GPUs or other accelerators. - Experience with systems automation, and with systems design and debug. - Experience working with vendors or customers. - Experience working with distributed systems, and familiarity with common solutions, design patterns, or best practices. - 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 IO technologies.