Technical Program Manager III, Quality Measurement, 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: $163000 - $237000 (USD) + 15% bonus target + equity + benefits

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

Responsibilities

• Ensure that Trust and Safety has the metrics and tools in place to proactively measure and detect quality-related issues throughout its expansive, AI-driven enforcement and labeling ecosystem.
• Own and maintain the quality measurement and reporting strategy for a high-complexity portfolio of product areas.
• Design and architect Human-over-the-Loop (HOTL) workflows and AI evaluation rubrics to ensure the consistent application of nuanced policies.
• Engage in the transition from manual QA to AI-driven predictive insights, anomaly detection, and systemic logic verification.
• Leverage AI co-pilots and tools to automate processes, write scripts, and prototype new safety and quality frameworks.