Staff Software Engineer, ML Infrastructure, Applied AI

### Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development focusing on infrastructure engineering in C++.
• 5 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), or specialization in another ML field.
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

### Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures and algorithms.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a complex organization involving cross-functional, or cross-business projects.

## 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.
Our team's mission is to build the next generation of enterprise software by establishing AI as its fundamental core. We are pioneering an Agentic AI-driven enterprise workforce, creating intelligent, autonomous systems that drive automation and unlock strategic value across high-value functions like Finance, Sales, Marketing, and Procurement. By serving a wide array of industries from Technical to Retail, we focus on AI innovation and bringing them into real-world solutions for most impactful challenges.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 $207,000-$300,000 + 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

• Architect and build high-performance, distributed infrastructure to support agentic AI workflows, leveraging C++ to ensure low-latency agentic systems for real-world enterprise loads.
• Take full ownership of the technology stack, transitioning experimental models into production services while ensuring system reliability, observability, and fault tolerance in multi-agent environments.
• Drive inference cost optimization and system efficiency by implementing efficient connectors, optimize kernels, manage memory usage, and reduce latency to ensure AI solutions are not just powerful, but economically viable and at scale.
• Provide technical guidance on system architecture and code quality, fostering a culture of engineering excellence through design reviews, code audits, and the adoption of best practices.
• Maintain a tight loop between hypothesis and deployment by quickly prototyping new capabilities and seamlessly harden them for production release while focusing customer needs.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.