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X In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:

• Health, dental, vision, life, disability insurance
• Retirement Benefits: 401(k) with company match
• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
• Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
• Maternity Leave (Short-Term Disability Baby Bonding): 28-30 weeks
• Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year

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).

About the job

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 team 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.
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 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.