Staff Software Engineer, Search Agentic Ecosystem Platform

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.

Join the Agentic Ecosystem team in Search and lead the transformation of Google’s AI from an "answer engine" into an "action engine". We are building the foundational platform that enables Search AI Mode and Gemini to help users reliably complete any task by intelligently invoking and taking action with a vibrant ecosystem of third-party services.

We leverage the Model Context Protocol (MCP), a standardized open protocol, to connect Google’s advanced AI models to external systems of data and tools. Your work will enable a "write once, run everywhere" integration model, allowing partners to reach billions of users across Search, Gemini, and Geo through a single unified platform.

In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Architect the core infrastructure for Agentic Integrations (focusing on MCP Servers) while designing collaborative solutions that expand the overall search experience framework and maintain a high bar for search quality.
Build domain-specific use cases atop generalist models (AIM/Gemini), actively helping vertical teams onboard onto the platform to deliver complex tool orchestration and high-trust, consistent user experiences.
Ensure integrations are surface-agnostic across AI Mode (AIM), Gemini, and Geo by guiding high-level architectural decisions and governing cross-platform technical direction.
Act as a Technical Lead, providing technical mentorship and leadership to a global engineering team, driving the successful execution of high-priority partner integrations across verticals.

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 ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience with Agentic integrations and Model Context Protocol.

Preferred qualifications:

Proven experience building developer-facing platforms, SDKs, or third-party integration frameworks.
Familiarity with LLM orchestration, tool-use (function calling), and agentic workflows. Direct experience with Model Context Protocol (MCP) is strongly preferred.
Passion for rapid prototyping and "hill-climbing" to solve for the nuances of specialized vertical domains.
Ability to navigate and lead in a complex, cross-functional environment, aligning technical strategies between different product areas.
Demonstrated quality assurance by executing evaluations while documenting successes and failures to identify areas for improvement.