Senior Software Engineer, Workspace Abuse and Safety Protections

Ensure the products remain trusted: private, secure, and safe.Provide safe and trustworthy experiences for hundreds of millions of users across Google Workspace applications. Develop, deploy, and scale machine learning systems to detect, prevent, and enforce against spam, phishing, and sophisticated abuse vectors across all Workspace collaboration, productivity, and real-time communication products—including Drive, Docs, Sheets, Slides, Calendar, Chat, Meet, Voice, Keep, Forms, and Sites.Grow critical ML-powered protections to make Workspace safe from various and constantly-evolving abuse vectors for our consumer and business users; drive the end-to-end ML strategy, modeling architectures, and enforcement pipelines that safeguard user content and interactions; leverage state-of-the-art techniques—including LLMs, and real-time classification to build enforcement and prevention solutions to stop harmful abuse.Minimum qualifications:Bachelor’s degree or equivalent practical experience.5 years of experience in applied machine learning.3 years of experience with generative AI (GenAI), large language models (LLM), machine learning, and related frameworks.1 year of experience in building generative AI or agentic applications.Experience leading technical architecture, cross-functional projects, or mentoring junior-to-mid engineers.Preferred qualifications:Experience in a safety, abuse-fighting or integrity role at a top Internet company.Experience partnering with Product Management, PR, UX and UI, Data Science, Analysts, Legal, and Operations.Experience leading and motivating teams towards business goals, while balancing innovation with stability and scalability.Ability to use a combined creative, data-driven and analytical approach to technical and business problems.Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models. Google Workspace from Google Cloud is a smart, simple and secure family of productivity apps – Gmail, Docs, 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, Docs, 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 - $252000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor’s degree or equivalent practical experience.5 years of experience in applied machine learning.3 years of experience with generative AI (GenAI), large language models (LLM), machine learning, and related frameworks.1 year of experience in building generative AI or agentic applications.Experience leading technical architecture, cross-functional projects, or mentoring junior-to-mid engineers.