Minimum qualifications:
- Bachelor’s degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages (e.g., Python, Kotlin, Java, Go, C++).
- 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 speech/audio, reinforcement learning (e.g., sequential decision making), ML infrastructure, or another specialized ML field.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
- Master's degree or PhD in Computer Science, Mathematics, or a related technical field.
- 5 years of experience with data structures/algorithms applied to client systems, developer infrastructure, or ML pipelines.
- 1 year of experience in a technical leadership role driving architectural standards.
- Experience with client validation platforms, mobile/web UI test automation (e.g., Android/iOS, Espresso), or closed-loop AI agent toolchains.
- Experience developing accessible technologies and UI verification patterns.
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.
The Client Validation team develops the AI-native verification platform and testing infrastructure powering Google’s mission-critical web, mobile, and multiplatform client applications. We build the foundational runtimes, browser/mobile automation systems, and visual assertion engines that ensure products used by billions of people daily remain fast, reliable, and visually flawless.
As AI coding assistants and autonomous code generation models accelerate software creation to superhuman speeds, the primary bottleneck in engineering has fundamentally shifted: writing code is no longer the hardest constraint--verifying that it works, looks right, and integrates cleanly across heterogeneous web and mobile ecosystems is.
In this role, you will lead the architecture and implementation of closed-loop agentic validation infrastructure. Rather than treating testing as a post-build step, your work transforms verification into an active, self-healing feedback loop.
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Write and test production software for agentic validation platforms, closed-loop execution harnesses, and autonomous client testing runtimes.
- Collaborate with peers and cross-functional stakeholders through design and code reviews to ensure testability, efficiency, code quality, and engineering best practices.
- Contribute to technical documentation and educational content, adapting materials based on agentic platform updates, spec-driven development standards, and user feedback.
- Triage, debug, and resolve complex product or system issues across web and mobile stacks, analyzing root causes and operational service quality.
- Design and implement solutions in specialized ML areas, leveraging ML infrastructure to advance semantic UI verification and self-healing test remediation.
