Senior Software Engineer, Workspace Abuse and Safety Protections

Google•Sunnyvale, CA

2d•$174,000 - $252,000

About The Position

Google Cloud builds AI/ML software that connects advanced models with products used at scale. Google Workspace is a family of productivity applications—including Gmail, Docs, Drive, Calendar, Sheets, Meet, Voice, Chat, and more—with real-time collaboration and generative AI features. This role focuses on keeping Workspace trusted, private, secure, and safe for users across its collaboration, productivity, and real-time communication products. The engineer will develop and scale machine learning protections against spam, phishing, and sophisticated, evolving abuse, shaping end-to-end ML strategy, model architectures, and enforcement pipelines. The role uses techniques including large language models and real-time classification to prevent harmful abuse, and offers the opportunity to work with Google DeepMind and influence products used by billions.

Requirements

Bachelor’s degree or equivalent practical experience.
5 years of experience in applied machine learning.
3 years of experience with generative AI, large language models, machine learning, and related frameworks.
1 year of experience building generative AI or agentic applications.
Experience leading technical architecture, cross-functional projects, or mentoring junior-to-mid engineers.

Nice To Haves

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 toward 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.

Responsibilities

Ensure 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 Workspace collaboration, productivity, and real-time communication products.
Grow machine-learning-powered protections against constantly evolving abuse vectors for consumer and business users.
Drive end-to-end machine learning strategy, modeling architectures, and enforcement pipelines that safeguard user content and interactions.
Use state-of-the-art techniques, including large language models and real-time classification, to build enforcement and prevention solutions that stop harmful abuse.