Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• Experience in designing sampling approaches and using statistical analysis for making product recommendations.
  

Preferred qualifications:

• Experience in tuning and applying Large Language Models for data labeling.
  
• Experience with driving data labeling quality improvements.
  
• Excellent written and verbal communication skills.
  
• 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.

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 diverse 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.

Responsibilities

• Develop domain knowledge in Search infrastructure, ranking signals, and products.
• Develop rating templates for evaluating and measuring content safety working with images, videos, pages, and queries.
• Contribute to prompt-based Large Language Models that can evaluate content safety according to our product policies.
• Design and implement product metrics to benchmark user trust risks and track improvements over time.
• Create datasets for engineers to evaluate and improve sensitive content classifiers.