Senior Software Engineer, Workspace Abuse and Safety Protections

About the role

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.

ABOUT THE JOB:

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 + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• 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.