Job Description

Minimum qualifications:

• Bachelor’s degree in computer science, mathematics, statistics, a related technical field, or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages (e.g., Python, C++, Java), or 1 year of experience with an advanced degree.
• 1 year of experience with one or more of the following: speech/audio technology, reinforcement learning, or specialization in another ML field.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, and debugging).
• Experience in writing unit and integration tests to ensure the accuracy and efficiency of production-level code.

Preferred qualifications:

• Master's degree or PhD in computer science, artificial intelligence, or a related technical field.
• 2 years of experience with advanced data structures, algorithms, and system design for high-throughput cloud services.
• Experience working with Large Language Models (LLMs), agentic workflows, or frameworks for autonomous AI agents.
• Experience building or maintaining services on distributed cloud platforms (e.g., Google Cloud Platform) and managing session persistence in stateful applications.
• Experience developing accessible technologies or tools that ensure AI-driven interfaces are usable by a range of users.

About the job

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.

Google’s Cloud AI team is at the forefront of the industry’s shift toward agentic AI. The team is building a platform that empowers developers to create, deploy, and scale the next generation of intelligent agents. The mission is to bridge the gap between foundation models and production-grade applications by providing high-performance infrastructure, flexible development kits, and session management. By collaborating with research and product teams across Google, we are defining the standard for AI agents capable of handling long-running enterprise workflows.

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 $147,000-$211,000 + 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

• Write high-quality product or system development code for scalable agentic infrastructure, focusing on performance and reliability in distributed environments.
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Contribute to and maintain technical documentation or educational content; adapt materials based on AI product updates and developer feedback.
• Triage system issues and debug, track, and resolve issues by analyzing the impact on network performance, service operations, and AI model quality.
• Implement solutions in specialized ML areas (e.g., agent orchestration), utilize ML infrastructure, and contribute to model optimization and data processing for generative AI.