Software Engineering Manager, Business AI Agent

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

The Business Agent team powers Google’s Agentic Commerce strategy by building the AI platform behind seamless merchant-consumer interactions across Search, YouTube, Ads, and Messaging. We turn conversational AI into measurable commerce outcomes for millions of businesses worldwide.

We solve complex, high-scale AI issues to build fast, reliable, and intelligent agent experiences. Optimize multi-turn AI reasoning loops and tool-calling pipelines for real-time responsiveness. Develop robust data pipelines for the Merchant Knowledge Library to power accurate Retrieval-Augmented Generation (RAG). Build automated evaluation frameworks and AutoRaters to continuously measure conversation quality and factuality at scale.

People shop on Google more than a billion times a day - and the Commerce team is responsible for building the experiences that serve these users. The mission for Google Commerce is to be an essential part of the shopping journey for consumers - from inspiration to to a simple and secure checkout experience - and the best place for retailers/merchants to connect with consumers. We support and partner with the commerce ecosystem, from large retailers to small local merchants, to give them the tools, technology and scale to thrive in today’s digital world.

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

• Manage and mentor a Software Engineering team, owning the roadmap and architecture for applied AI, low-latency scoring, and automated evaluation.
  
• Lead Knowledge Library and merchant control engineering, building advanced RAG integrations (PDF ingestion, auto-learning) for custom agent behavior.
• Oversee autonomous execution tooling and Universal Commerce Protocol (UCP) integration to enable active user journeys (catalog lookups, universal carts, reservations).
• Advocate prompt tuning and loss diagnosis while scaling LLM-based AutoRater frameworks to reduce hallucinations and measure conversation factuality.
• Partner with Product, Data Science, UX, and core Google platform teams to define Ground Truth datasets, analytical dashboards, and model upgrade standards.

Minimum qualifications:

• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
• 8 years of experience coding in one or more general-purpose programming languages (e.g., Python, Java, C++, or Go) focused on backend system architecture.
• Experience building automated LLM evaluation pipelines, automated rating systems (AutoRaters), or defining LLM-specific reliability metrics (e.g., measuring grounding, helpfulness, safety, and hallucination/error reduction).
• Experience with backend, distributed systems, or AI/ML systems infrastructure.
• Experience managing software engineers, including performance management, mentoring, and team growth.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
  
• 3 years of experience working in a complex, matrixed organization.
  
• Experience developing, calibrating, and productionizing LLM-based AutoRater frameworks or automated regression testing.
• Experience designing agentic tooling and integrating agents with commerce platforms.
• Expertise in conversational AI architectures, agent orchestration harnesses, and Retrieval-Augmented Generation (RAG) for knowledge libraries.
• Track record of technical leadership partnering with Product Management, Data Science, and UX to build "Ground Truth" datasets and performance-tracking dashboards.

In most instances, this position requires in-person interviews as part of the hiring process.