Our AI Infrastructure Engineering Support team is dedicated to ensuring our customers get the most out of their Google Cloud hardware investment. As a Field Application Engineer Manager (Hardware Engineer), your team will serve as on-site, external-facing trusted advisors to customers.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $236000 - $329000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Lead a team that will participate in on-call activities, working with the primary responders to resolve system observations. Provide leadership, mentorship, and career development for team members.
- Manage customer problems through effective diagnosis, resolution, or implementation of new investigation tools to increase productivity on AI/ML infrastructure.
- Work closely with multiple Product, Quality, and Engineering teams to improve the product, and 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:
- Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or IT-related field, or equivalent practical experience.
- 8 years of experience with Linux/Unix systems and experience in debugging issues across the hardware/software boundary on enterprise-grade server infrastructure.
- 5 years of experience in technical leadership.
- 3 years of experience with technical infrastructure (e.g., deployment, maintenance, and troubleshooting), and with quality and reliability of technical infrastructure.
- 3 years of debug or validation experience with CPU, GPU, or TPU.
- Experience troubleshooting and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, or performance).
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 with ML frameworks (e.g., TensorFlow, PyTorch), with the understanding of the AI/ML training and inference life-cycle.
- Experience working with 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-premises or cloud environment.
- Understanding of memory and high-speed IO technologies.