Staff Software Engineer, Data and Model Insights and Infrastructure

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.

• 8 years of experience in software development.

• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

• 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), ML infrastructure, 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).

• Experience integrating generative AI tools or LLM interfaces into workflows.

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, matrixed organization involving cross-functional, or cross-business projects.

• 2 years of experience with technical innovation in data governance or related fields.

• Experience with machine learning and privacy data handling.

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.

As a Staff Software Engineer in AI/ML, you will be a technical cornerstone for our engineering organization. You won't just be writing code, you will be defining the technical trajectory for complex, large-scale machine learning systems that impact Google’s global infrastructure. You will bridge the gap between research and production-grade engineering, ensuring our AI solutions are scalable, efficient, and deeply integrated into our core products. Your leadership will influence not only your immediate team but also cross-functional partners in Product, Research, and UX.

The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities:

• Lead the design and architecture of highly complex ML pipelines and distributed systems, ensuring they meet long-term scalability and reliability goals.

• Drive the adoption of next-generation AI/ML techniques to solve ambiguous, high-impact business problems, moving from conceptual prototypes to global production.

• Set the "gold standard" for code quality, testing, and deployment within the team, performing deep-dive architectural reviews and optimizing system performance.

• Mentor executive and mid-level engineers, fostering a culture of technical growth, and represent the team in high-level technical discussions across the broader organization.

• Partner with researchers to operationalize models and work with product stakeholders to align technical roadmaps with user needs.