Scaled Testing Specialist, Responsible AI, Trust and Safety

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 7 years of experience in data analytics, Trust & Safety, policy, cybersecurity, or related fields.
  

Preferred qualifications:

• Master's degree or PhD in relevant field.
• Education in, or experience with, machine learning.
  
• Experience in SQL, building dashboards, data collection/transformation, visualization/dashboards, or experience in a scripting/programming language (e.g. Python).
  
• Strong understanding of AI systems, machine learning, and their potential risks or experience working with Google's products and services, particularly GenAI products.
• Excellent communication and presentation skills (written and verbal) and the ability to influence cross-functionally at various levels.
  
• Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment.

About the job:

Trust & Safety team members are tasked with identifying and taking on the biggest problems that challenge the safety and integrity of our products. They use technical know-how, excellent problem-solving skills, user insights, and proactive communication to protect users and our partners from abuse across Google products like Search, Maps, Gmail, and Google Ads. On this team, you're a big-picture thinker and strategic team-player with a passion for doing what’s right. You work globally and cross-functionally with Google engineers and product managers to identify and fight abuse and fraud cases at Google speed - with urgency. And you take pride in knowing that every day you are working hard to promote trust in Google and ensuring the highest levels of user safety.

In this role, you will be an expert in structured and unstructured safety pre-launch testing for Google's GenAI models and products, with a particular focus on the under-18 experience. You will partner closely with the technical abuse-fighting experts in trust and safety to understand launch requirements, and develop and implement testing protocols. You will leverage robust data analysis to provide quantitative and qualitative actionable insights on potential risks for mitigation by T&S and product teams. You will manage many stakeholders through effective relationship building, bringing an ordered and structured approach. You'll demonstrate analytical thinking through data-driven decision making and technical know-how, to collaborate across teams and execute quickly. This will be instrumental in shaping the future of AI development, ensuring that Google's AI products are safe for all users.

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.

The US base salary range for this full-time position is $132,000-$194,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities:

• Own the pre-launch U18 testing lifecycle for prominent GenAI products, from aligning on compliance standards and safety guidelines to final execution, ensuring consistency across all product areas.
• Define prompt generation and scraping strategies using LLM tools and vendor teams to rigorously test model boundaries, compliance, and potential risks.
• Conduct deep-dive qualitative and quantitative analyses of test results to identify edge cases and provide actionable mitigation strategies that inform critical pre- and post-launch decision-making.
• Build reusable frameworks, operational norms, and best practices for red teaming and AI safety to scale Responsible AI (RAI) testing efficiency and impact cross-functionally.