Staff Software Engineer, AI/ML GenAI, Cloud Applied AI

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience testing, and launching software products.
• 3 years of experience with software design and architecture.
• 2 years of experience with state of the art Generative AI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with Genwrative AI-related concepts (language modeling, computer vision).

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 6 years of experience in Applied Machine Learning, in teams directly responsible for business metrics on user-facing products.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  
• 3 years of experience working in a structured organization involving cross-functional or cross-business projects.
• Experience in designing APIs for external customers.
• Experience in modern full-stack application development, spanning frontend technologies (TypeScript, JavaScript, HTML/CSS, Angular, React) and backend services (e.g., Java, Kotlin).

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Applied AI builds conversational agents deployed at a large scale that achieve very meaningful results in the real world. Some examples include the customer agent built for large call center environments, to fast food ordering handled by our Food AI agent. The team is transforming how enterprises connect with customers through the power of AI. We also offer unique experiences for team members where you get to work directly with the model builders (Google DeepMind / Vertex), learn and work with brilliant AI leaders, and have access to Global 1000 customers via our existing Google Cloud relationships. The opportunity in this space is tremendous.

The US base salary range for this full-time position is $197,000-$291,000 + 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.

Responsibilities

• Drive the end-to-end technical strategy, architecture, and execution for complex, ambiguous projects on our conversational AI platform. Define the technical roadmap for scalable, long-term solutions.
• Act as the lead engineering partner for Product Management and UX. Go beyond just understanding requirements to actively define product strategy, refine ambiguous customer needs into concrete technical designs, and drive consensus across teams.
• Manage and iterate on new platform capabilities to meet immediate customer needs. Balance this speed with long-term technical health, advocating best practices to ensure solutions are clean, maintainable, scalable, testable, and easy to refactor.
• Leverage user empathy to guide product direction, respond thoughtfully to customer feedback, and build intuitive, powerful, and seamless experiences for the developers who rely on our platform.