Customer Engineer, AI/ML, SAISV, Google Cloud

Customer Engineer, Ai/Ml, Saisv, Google Cloud

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 work closely with technical sales teams as a subject matter expert in AI/ML to showcase the power of Google Cloud to our customers. You will help both prospective and existing customers and partners understand the capabilities of Google Cloud, design creative solutions and architectures to address their business challenges, lead proofs of concept, and troubleshoot technical issues. You will collaborate with internal engineering teams to improve products and build solutions that optimize results in production. You will also find innovative ways to expand your impact and the team's overall success. You will be part of a team of Googlers, working in an environment that fosters respect and opportunities for all.

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 $125,000-$183,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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.

Minimum Qualifications:

• Bachelor's degree or equivalent practical experience.
• 6 years of experience with cloud native architecture in a customer-facing or support role.
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.
• Experience with Machine Learning model development and deployment.
• Experience with frameworks for deep learning (e.g., PyTorch, Tensorflow, Jax, Ray, etc.) and using machine learning APIs.

Preferred Qualifications:

• Master's degree in Computer Science, Engineering, Mathematics, a technical field, or equivalent practical experience.
• Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g., deep learning, LSTM, convolutional networks).
• Experience in architecting and developing software or infrastructure for scalable, distributed systems.
• Experience in data and information management.
• Ability to learn, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.

Responsibilities:

• Work with the team to identify and qualify business opportunities, understand customer technical objections, and develop the strategy to resolve technical blockers.
• Share AI/ML expertise to support the technical relationship with customers, including technology advocacy, supporting bid responses, product and solution briefings, proof-of-concept work, and partnering directly with product management to prioritize solutions impacting customer adoption to Google Cloud.
• Work directly with Google Cloud products to demonstrate and prototype integrations in customer and partner environments.
• Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to successfully implement a complete solution on Google Cloud.
• Travel to customer sites, conferences, and other related events as required, acting as a public advocate for Google Cloud.