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.
We are seeking a Senior Software Engineer to drive the technical direction and architecture of our Agentic Ecosystem Platform. In this role, you will lead the design, development, and scaling of robust infrastructure for agentic operations, addressing complex third-party integrations before expanding to support core first-party systems.
You will navigate the ambiguity of the agentic paradigm shift, building highly reliable, universally accessible platforms that power next-generation capabilities across major Google products (including AIM, Gemini, Geo, and Shopping). You will partner deeply with cross-functional product teams to define technical strategy, expand platform capabilities, set engineering best practices, and elevate the team's technical execution.
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.****
Responsibilities
- Develop complex, domain-specific implementations on top of generalist models (AIM/Gemini). Act as the primary technical anchor for onboarding vertical teams, writing critical-path code and designing tool orchestration to ensure a consistent, high-trust user experience.
- Translate broad architectural goals into concrete, surface-agnostic systems across AIM, Gemini, and Geo.
- Lead the technical delivery of high-priority partner integrations within a globally distributed team.
- Drive the technical implementation of the MCP2Agency bridge and ensure strict adherence to data governance policies (e.g., SIAN) in the codebase.
- Act as a technical role model by multiplying team impact through rigorous code reviews, leading design document reviews, and mentoring engineers.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 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.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
- Experience building developer-facing platforms, software development kits, or third-party integration frameworks.
- Experience with Model Context Protocol (MCP).
- Familiarity with Large Language Models (LLM) orchestration, tool-use (function calling), and agentic workflows.
- Ability to navigate and lead in a complex, cross product area environment, aligning technical strategies between Search and Gemini teams.
- Ability to execute evaluations while documenting successes and failures to identify areas for improvement.
- Ability to investigate horizontal solutions to mitigate recurring losses and apply various optimization techniques to refine model performance.