Staff Software Engineer, Workspace Abuse Safety Protection

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.

Workspace Abuse Safety Protection works on identifying bad actors on Workspace platform, and blocking the abuse by various enforcement strategies.

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.

Responsibilities

• Combat abuse on an agentic framework across all Workspace products (Gmail, Drive, Calendar, Chat, Meet, Voice, etc.).
• Build out a robust data pipeline across Workspace and actor signals (consumer, enterprise, agents).
• Work on horizontal signals from user level to population level.
• Deploy models to identify risks throughout the lifecycle of bad actors (account creation to abuse attacks).

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience in testing, and launching software products.
• 5 years of experience with one or more of the following such 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.
• 5 years of experience in leading technical project strategy, Machine Learning (ML) design, and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 3 years of experience with software design and architecture.

Preferred qualifications:

• Master’s degree or PhD in engineering, computer science, or a related technical field.
• 5 years of experience deploying production/operational ML systems.
• Experiences in building production agentic systems.