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Job Details
Posted date: Apr 17, 2026
There have been 3 jobs posted with the title of Staff Software Engineer, Generative AI, Data Analytics all time at Google.Location: Kirkland, WA
Level: Director
Estimated salary: $253,500
Range: $207,000 - $300,000
Description
Lead the technical strategy and architectural design of the core reasoning engine that translates natural language into reliable SQL insights, ensuring the platform scales to support complex enterprise data exploration. Drive cross-functional collaboration with AI/ML, UX, and Product teams to define the "agentic" future of BigQuery, bridging the gap between raw data and business-ready answers. Establish and maintain engineering excellence by setting the bar for performance, reliability, and observability of production-critical agent services across the BigQuery ecosystem. Mentor and influence a broad group of engineers, identifying and refining ambiguous, high-impact problems into tractable projects that advance our data-centric AI capabilities.Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models.
We build the industry's best data agents to help customers make more, better, and faster data-driven decisions—achieved by enriching the customer knowledge layer, automating data preparation, providing tailored agent harnesses, and leveraging the advanced capabilities of BigQuery and its ecosystem.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving channel behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
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.
Qualifications
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.Preferred qualifications: Experience reading code in one or more languages such as Java, Python, NodeJS, Golang and exposure to GenAI / AI Agent-based architectures in solution design. Experience with AI/ML techniques including Recommender Systems, Generative AI, Large Language Models, Information Retrieval, etc. Understanding of the modern Data and AI landscape, across Generative AI, Vector Databases, Retrieval-Augmented Generation (RAG), and the practical application of Data Agents to solve enterprise data challenges (e.g., conversational analytics, text-to-SQL, automating data science/AI workflows).