Senior Technical Program Manager, Quality Measurement Lead, Trust and Safety

About the job

A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You’re equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers.

At Google we work hard to earn our users’ trust every day. Trust & Safety is Google’s team of abuse fighting and user trust experts working daily to make the internet a safer place. We partner with teams across Google to deliver bold solutions in abuse areas such as malware, spam and account hijacking. A team of Analysts, Policy Specialists, Engineers, and Program Managers, we work to reduce risk and fight abuse across all of Google’s products, protecting our users, advertisers, and publishers across the globe in over 40 languages.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $192000 - $279000 (USD) + 20% bonus target + equity + benefits

Learn more about [benefits at Google](https://www.google.com/about/careers/applications/benefits/).

Responsibilities

• Architect and drive the strategic roadmap for T&S quality's transition to AI-driven process automation and workflow optimization.
• Own the design and execution of quality automation. Build scalable, AI-enabled workflows that integrate across product areas while partnering with T&S leadership to align automation initiatives with global safety infrastructure and business goals.
• Establish and lead the programmatic framework for identifying, prototyping, and deploying AI/ML solutions (including Large Language Models (LLMs) and GenAI) to automate business-as-usual (BAU) tasks, such as project planning, intake, portfolio tracking, quality monitoring, and ongoing maintenance.
• Scale the adoption of AI agents across the organization, establishing best practices, training, and frameworks for agentic workflow development.
• Define the cross-functional engagement model and align technical dependencies with Tooling, QA Automation, Engineering and other cross-functional teams to integrate scalable automation tools into a cohesive, shared platform.