Senior Software Engineering Manager, Cloud AI, Agents

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
  
• 7 years of experience leading technical project strategy, ML design, and optimizing industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 5 years of experience in a technical leadership role; overseeing projects.
• 5 years of experience in a people management, supervision/team leadership role.
• 2 years of experience with GenAI techniques (e.g., Large Laguage Models, Multi-Modal, Large Vision Models) or with GenAI-related concepts (e.g., language modeling, computer vision).

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 5 years of experience working in a cross-functional organization.

About the job

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.

Our mission is to engineer the platform for building and running AI agents on Google Cloud. We are at the heart of Google Cloud's strategic pivot to an agent-first world, creating the foundational platform where intelligent, autonomous agents will operate, and scale. We are moving beyond traditional software applications and creating the Agent-Platform-as-a-Service that will empower developers to build sophisticated Agentic applications capable of performing, multi-step tasks.

The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.

The US base salary range for this full-time position is $248,000-$349,000 + 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.

Responsibilities

• Provide technical leadership and direction for the team designing and building our core agentic runtime. This includes solving first-principle challenges in Large Language Model (LLM) based planning, stateful execution for long-running tasks, and building a fault-tolerant orchestration engine for non-deterministic workflows.
• Oversee the architecture and delivery of the developer-facing components of the platform, which  includes the SDKs, APIs, and low-code visual surfaces that developers will use to define agent goals, provide tools (connectors), and build, test, and deploy their AI agents on Google Cloud.
• Drive the strategy for critical agent infrastructure. Partner with Product Management and AI research teams to build solutions for agent observability (debugging and tracing), evaluation frameworks (testing agent reliability), and security sandboxing to ensure safe tool usage.