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Field Application Engineer Manager, Cloud AI Infrastructure

Google, Austin, Texas +1

Posted pay

$236K–$329K

a year

Location: Austin, Texas +1
Work type: Onsite
Level: Not given, manager
Experience: 8+ years
Skills in the job text: Kubernetes
PyTorch
Linux
TensorFlow
Posted: 14 days ago
Last check: 4 days ago
Job type: ML / AI engineering, Inference & ML systems

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Google

About Google: funding, hiring and pay

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The role

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 [https://www.google.com/about/careers/applications/benefits/].

What you will do

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.

What you need

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).

Nice to have

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.

Read the job posting on the Google site

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Source: the company job board. We last checked this job 4 days ago. We first saw it 4 days ago. A job on a job board does not always mean that the team interviews people now.

Google

Open tech jobs: 681
With posted pay: 74%
Median posted base: $174K–$252K

About Google: funding, hiring and pay

Compared with similar jobs

This base pay is above the median for ML and AI engineering jobs in other US places with no level in the job title.

Median: $182K a year
Other jobs in the group: 417

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.