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Open nowFirst seen 4 hours ago

Customer Engineer I, AI Infrastructure, Google Cloud

Google3,277 open roles

Where
Sunnyvale, CA, USA; Atlanta, GA, USA; Austin, TX, USA; Boulder, CO, USA; Cambridge, MA, USA; Chicago, IL, USA; Addison, TX, USA; Kirkland, WA, USA; New York, NY, USA; Reston, VA, USA; Seattle, WA, USA; San Francisco, CA, USA
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Your applicationOpen nowCustomer Engineer I, AI Infrastructure, Google CloudGoogle · Sunnyvale, CA, USA; Atlanta, GA, USA; Austin, TX, USA; Boulder, CO, USA; Cambridge, MA, USA; Chicago, IL, USA; Addison, TX, USA; Kirkland, WA, USA; New York, NY, USA; Reston, VA, USA; Seattle, WA, USA; San Francisco, CA, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Google's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

8.2% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 26 days.

Share of postings closed within
  1. 1.8%1 day
  2. 3.6%3 days
  3. 8.2%7 days
  4. 15.2%14 days
  5. 34.0%30 days
This job: first seen 4 hours ago

Google median: 26 days open

The posting

The application window will be open until at least October 21, 2026. This opportunity will remain online based on business needs which may be before or after the specified date.

Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.

In 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: Sunnyvale, CA, USA; Atlanta, GA, USA; Austin, TX, USA; Boulder, CO, USA; Cambridge, MA, USA; Chicago, IL, USA; Addison, TX, USA; Kirkland, WA, USA; New York, NY, USA; Reston, VA, USA; Seattle, WA, USA; San Francisco, CA, USA.

Minimum qualifications:

  • Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
  • 4 years of experience with cloud infrastructure.
  • Experience building and operationalizing machine learning models.
  • Experience in delivering technical presentations, leading discovery and planning sessions.

Preferred qualifications:

  • Experience training and fine tuning large models (i.e., image, language, segmentation, recommendation, genomics) with accelerators.
  • Experience with performance profiling tools (i.e., TensorFlow profiler, PyTorch profiler, Tensorboard).
  • Experience designing/architecting large-scale infrastructure farms for specialist AI use cases.
  • Experience with containerization, Kubernetes, Kubernetes on Google Cloud.
  • Experience with machine learning benchmarks.
  • Ability to engage with C-level or executive business leaders and influence decisions.

About the job

When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.

As a Practice Customer Engineer (CE) specializing in AI Infrastructure, you will partner with technical Sales teams to incubate, pilot, and deploy Google Cloud’s industry-leading AI/ML accelerators (TPU/GPU) for AI startups and large enterprises. You will help customers innovate faster using Google Cloud’s flexible, open infrastructure.

In this role, you will identify large-scale AI opportunities and integrate accelerators into customers’ cloud strategies by running model benchmarks, developing migration paths, and analyzing cost-to-performance. Partnering with Platform CEs, you will write code to build prototypes, proofs-of-concept, and demos that solve complex AI challenges and influence product development.

You will gather business and technical requirements to persuasively present practical solutions that prove the value of the Google Cloud portfolio.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. 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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $104000 - $150000 (USD) + 42.86% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Be a trusted advisor to our customers, helping them understand and incorporate AI accelerators into their overall cloud strategy by recommending migration paths, integration strategies, and application architecture that incorporate Google Cloud AI optimized infrastructure.
  • Demonstrate how Google Cloud is differentiated, highlighting the power of accelerators by working with customers on proof of concepts, demonstrating features, optimizing model performance, profiling, and benchmarking.
  • Build repeatable assets to enable other customers and internal teams.
  • Influence Google Cloud strategy at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements.
  • Travel to customer sites and events as needed.
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