Minimum qualifications: Bachelor’s degree or equivalent practical experience. 5 years of experience with software development in one or more programming languages. 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). 3 years of experience in Applied Machine Learning. 3 years of experience with performance optimization, systems data analysis, visualization tools, or debugging. 1 year of experience in GenAI/LLM within teams responsible for business metrics on user-facing products. Preferred qualifications: Master's degree or PhD in Computer Science or related technical field. 5 years of experience with data structures and algorithms. 1 year of experience in a technical leadership role. Experience developing accessible technologies. 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 Workspace from Google Cloud is a smart, simple and secure family of productivity apps – Gmail, Documents, Drive, Calendar, Sheets, Meet, Voice, Chat and more. They are designed to simplify work and increase the productivity of teams, with real-time collaboration and GenAI features built in. With Google Workspace, information can flow freely between devices, apps, people and teams, so great ideas never get left in the margins again. In this era of hybrid work users are collaborating more and more via the beloved Google Workspace products such as Gmail, Documents, Sheets, Drive and Meet. This creates an opportunity to make Google Workspace the hub for all productivity work by transforming Workspace into a platform that powers a seamless experience for enterprise users and consumers. AI will change the future of work in profound ways, and our products— Gmail, Docs, Drive, Calendar, Sheets, Vids and Meet are at the forefront. From pre-computed summaries for email threads, summaries for meetings, and videos created from a document using lifelike AI avatars, our AI opportunity is huge. Our mission is to meaningfully connect people so they can create, build, and grow together and as part of the team you can build how productivity tools should work 5-10 years into the future. You will work with model builders (Google DeepMind), work with exceptional leaders, and have the ability to impact billions of users across the world.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities 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. Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field. Design, run, and evaluate experiments for training large language models, deep learning, and graph mining models with the goal of improving Gmail abuse detection on actor space.