Customer Engineer I, AI Infrastructure, Google Cloud
Google· startup· Internet & Cloud Services· Oct 7, 2026 · 21d ago
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
In office
Full-time
Mid
$144K–$195K est.
4+ years exp
Job description
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 .
• 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.
• 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.
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.
Application prep
Use the role’s language and application details while tailoring your materials.
Keywords to mirror
aimlmachine learningkubernetes
- Platform
- Effort
- medium