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XIn accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:- 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
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Austin, TX, USA; Kirkland, WA, USA.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, dGPU, 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 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.
- Familiarity with containerization and orchestration technologies like Kubernetes or Slurm in an on-prem or cloud environment.
About the job
Google Cloud accelerates organizations’ ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google’s technology – all on the cleanest cloud in the industry. 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.
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.
