Customer Engineer, Machine Learning, Google Cloud

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 10 years of experience with cloud native architecture, including experience architecting MLOps solutions for Machine Learning model development and deployment, in a customer-facing or support role.
• Experience with frameworks for deep-learning (e.g., PyTorch, Tensorflow, Jax, Ray, etc.), AI accelerators (e.g., TPUs and GPUs), model architectures (e.g., encoders, decorders, transformers), and using machine learning APIs.
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.

Preferred qualifications:

• Master's degree in Computer Science, or a related technical field.
• Experience in virtualization or cloud native architectures in a customer-facing or support role.
• Experience in architecting and developing software or infrastructure for scalable, distributed systems.
• Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g., LLM, deep learning, convolutional networks).
• Experience in data and information management as it relates to big data trends and issues within businesses.
• Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.

About the job

As a member of the Google Cloud team, you inspire leading companies, schools, and government agencies to work smarter with Google tools like Google Workspace, Search, and Chrome. You advocate for the innovative power of our products to make organizations more productive, collaborative, and mobile. Your guiding light is doing what’s right for the customer, you will meet customers exactly where they are at and provide them the best solutions for innovation. Using your passion for Google products, you help spread the magic of Google to organizations around the world.

As a Customer Engineer, you will work with technical Sales teams as a machine learning subject matter expert to differentiate Google Cloud to our customers. You will help prospective customers and partners understand the power of Google Cloud, explaining technical features, helping customers design architectures, and problem-solving any potential roadblocks. You will also have the opportunity to help customers to leverage specialized machine learning (ML) hardware developed by Google, called Tensor Processing Unit (TPU).

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 $142,000-$214,000 + 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.

Responsibilities

• Assist prospective customers and partners to understand the power of Google Cloud, explain technical features, help customers design architectures, and problem-solve any potential roadblocks.
• Influence the strategy for Google Cloud AI/ML by advocating for enterprise customer requirement and needs.
• Demonstrate the business value of Google Cloud AI/ML solutions that meet, enhance, and innovate for our enterprise customers.
• Travel to customer sites and events up to 40% of the time, as needed.
• Work with the team to identify and qualify business opportunities, understand key customer technical objections, and develop the strategy to resolve technical blockers.