Customer Engineer II, AI Infrastructure, Google Cloud

Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
• 5 years of experience with cloud infrastructure.
• Experience building and operationalizing machine learning models.
• Experience in delivering technical presentations, leading discovery and planning sessions that are aligned jointly with the customer with defined scope and success criteria.

Preferred qualifications:

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

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 Customer Engineer, you will partner with technical Sales teams as a subject matter expert in Artificial Intelligence and Machine Learning (AI/ML) to differentiate Google Cloud to our customers. You will help prospective and existing customers and partners understand the power of Google Cloud, develop creative cloud solutions and architectures to solve their business challenges, engage in proofs of concepts, and troubleshoot any technical questions and roadblocks.

In this role, you will use your expertise and presentation skills to engage with customers to understand their business and technical requirements, and present practical and useful solutions on Google Cloud. You will have excellent technical, communication and organizational skills. You will partner with internal engineering stakeholders to improve products and build solutions, optimizing for results when in production and identifying innovative ways to multiply your impact and the impact of the team as a whole. You will be a part of a diverse team of fellow Googlers working in an environment of respect and inclusion where we promote equal opportunities to succeed.

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.

The US base salary range for this full-time position is $122,000-$180,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or 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.