Software Engineer III, Infrastructure, Vertex Agent Platform

Software Engineer III, Infrastructure, Vertex Agent 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.

Vertex Agent Platform team delivers the industry's leading, enterprise ready agent platform that can power real world agentic use cases, along with state-of-the-art exemplar agents. We build a high-code agent development SDK/Kit (also known as ADK), a managed runtime (e.g., Agent Engine) with a suite of managed services (e.g. Session, Memory, ExampleStore, Sandbox, etc), agent inner-loop server (that brings the high-ceiling agentic capabilities of Gemini). We also worked with Orcas and GDM model quality teams to upstream the enterprise agentic use cases to our foundation model team. Internally, we partnered with many teams across Google such as Agentspace, CES, AI Studio, Labs, etc to bring the cutting-edge and cohesive agent platform that powers use cases such as data science agent, research agent, computer use agent, etc.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud's Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The US base salary range for this full-time position is $141,000-$202,000 + 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.

The minimum qualifications for this position include:

• Bachelor's degree or equivalent practical experience.
• 2 years of experience with software development or 1 year of experience with an advanced degree in an industry setting.
• 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)

Preferred qualifications include:

• Master's degree or PhD in Computer Science or related technical fields.
• Experience developing large-scale applications on Cloud.
• Experience on AI Agents, embedding models, in-context learning, evaluation and Open Source technologies in Generative AI space (e.g., LangChain).
• Experience with Generative AI Agents.
• Experience with Python and Java, with excellent coding skills.

Responsibilities include:

• Write product or system development code.
• Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
• Design and implement scalable, performant and compliant managed code execution sandbox service.
• Design and implement scalable, performant and compliant next-generation agent runtime services.
• Partner with quality team to evaluate and validate the agent features.