GenAI Large Language Models Solutions Engineer, gTech

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
  
• 3 years of experience coding with one or more programming languages (e.g., Java, C/C++, Python).
  
• 3 years of experience in a technical support/customer-facing role.
  
• Experience working with large language models/natural language processing Application Programming Interface (API), as well as working with Artificial Intelligence (AI) products, technologies, and cloud infrastructure.
  

Preferred qualifications:

• Master’s degree in Engineering, Computer Science, or a related technical field.
  
• Experience architecting and developing software or infrastructure for scalable, distributed systems.
  
• Experience in understanding data and information management as it relates to big data trends and issues within businesses.
  
• Experience building machine learning solutions and leveraging specific machine learning architectures (e.g., deep learning, Long Short-Term Memory (LSTM), convolutional networks).
  
• Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT Technology space.
  

About the job

In gTech Users and Products (gUP), our mission is to advocate for Google’s users by creating helpful and trusted experiences across the product ecosystem. We achieve this by meeting partners and consumers where they are with support and help, representing their needs with our product partners and proposing fixes and features that elevate their engagement with Google's diverse product ecosystem. Additionally we provide a range of product services that ensure our products are optimized for every user, no matter where they are in the world (e.g., localization, digitization, partner integration, and more).

In this role, you will be working as a machine learning subject matter expert responsible for providing technical support to our partners and developers who are using our large language models. This includes answering questions, troubleshooting issues, and providing training on how to use the models. You will also be responsible for working with our product team to gather feedback from our partners and help us improve our products and services.

Google creates products and services that make the world a better place, and gTech’s role is to help bring them to life. Our teams of trusted advisors support customers globally. Our solutions are rooted in our technical skill, product expertise, and a thorough understanding of our customers’ complex needs. Whether the answer is a bespoke solution to solve a unique problem, or a new tool that can scale across Google, everything we do aims to ensure our customers benefit from the full potential of Google products.

To learn more about gTech, check out our video.

The US base salary range for this full-time position is $118,000-$174,000 + 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 for new hire 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

• Support developers, creators, and enterprises to leverage Google’s Generative Language APIs so they can build their own AI products in the future.
  
• Provide in-depth machine learning expertise to support the technical relationship with Google’s developers and partners, including proof-of-concept work. Partner directly with product management to prioritize solutions impacting customer adoption.
  
• Answer questions about the models, their capabilities, and how to use them. Troubleshoot issues with the models and help partners resolve them.
  
• Stay up-to-date on the latest developments in large language models.
  
• Gather feedback from our partners and work with our product team to improve our products and services.