Software Engineer III, AI and Data Protection, Google Cloud

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 AI and Data Protection team engineers maintain the high-throughput, high-availability inspection services that power Google's safety capabilities. Our technology is fundamental to Cloud Sensitive Data Protection (Cloud SDP), Gmail/Chrome Enterprise/Google Drive's Data Loss Prevention (DLP) frameworks, AI (LLMs and Agents) inference safeguards with Model Armor, and a wide range of other Google offerings. We also perform storage scanning (GCS, BQ, etc), both thorough examination (reading every byte) and discovery (sampling records to create aggregate profiles). In this role, you will build the architecture, APIs, algorithms, and neural networks that provide fast, scalable classification and redaction of sensitive information and AI threat surfaces, both at runtime and at rest. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Write product or system development code. Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency). Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality. Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing. Minimum qualifications: Bachelor’s degree or equivalent practical experience. 2 year of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). 1 year of experience in software development with Java or Kotlin. Experience in infrastructure, platforms, or backend software engineering. Preferred qualifications: Master's degree or PhD in Computer Science or related technical fields. 2 years of experience with data structures and algorithms. Experience programming in Kotlin. Working experience on Google Cloud Platform. Bachelor’s degree or equivalent practical experience. 2 year of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). 1 year of experience in software development with Java or Kotlin. Experience in infrastructure, platforms, or backend software engineering.