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Open nowFirst seen 2 hours ago

Senior Cloud AI Abuse and Security Analyst

Google3,474 open roles

Where
Washington D.C., DC, USA
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Your applicationOpen nowSenior Cloud AI Abuse and Security AnalystGoogle · Washington D.C., DC, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Google's own form.

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The clock on this job

Early applications get read.

12.4% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 29 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: first seen 2 hours ago

Google median: 29 days open

The posting

In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:

  • Health, dental, vision, life, disability insurance
  • Retirement Benefits: 401(k) with company match
  • Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
  • Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
  • Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
  • Baby Bonding Leave: 18 weeks
  • Holidays: 13 paid days per year

Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 5 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
  • 5 years of experience managing projects and defining project scope, goals, and deliverables.

Preferred qualifications:

  • MS or PhD in a technical discipline like Computer Science, Statistics, Mathematics, or Operations Research.
  • Experience in domains such as anomaly detection, security analysis, time-series analysis, or metrics and reporting.
  • Proficiency in one or more programming language such as Python, Go, R, Julia, C/C++, Java, or SQL.
  • Solid understanding of generative AI, Large Language Models (LLMs), and AI agents, with security threat detection experience.
  • Solid problem-solving, critical thinking, and attention to detail in a dynamic environment.

About the job

The Trust and Safety Cloud AI team is the vanguard of Cloud AI security and safety. Our mission is to safeguard users, protect the integrity of our platforms, and ensure the responsible deployment of AI products at global scale. We operate at the critical intersection of AI research and real-world security, building the foundational defenses that prevent the misuse of generative models and agents. By pioneering proactive threat detection and robust mitigation strategies, we empower Google Cloud to push the boundaries of AI innovation safely, ethically, and securely.

You will play a vital role in ensuring that Google Cloud’s AI products are not only powerful but also safe, secure, and trustworthy. Your deep investigative skills, understanding of potential security harms, and passion for user protection will help the teams solve challenging problems in AI security and abuse at scale.

In this capacity, you will proactively identify and mitigate emerging threats within the AI landscape, ranging from adversarial attacks to potential misuse of generative technologies. Your work will involve synthesizing large datasets to uncover patterns of abuse, ensuring that Google's AI deployments remain resilient against sophisticated exploitation while upholding our responsibility for safe AI growth.

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

US: $159000 - $230000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Develop and deploy scalable safety solutions for Cloud AI using advanced machine learning.
  • Analyze data to identify infrastructure vulnerabilities and patterns of AI abuse.
  • Evaluate generative and agentic AI risks through benchmarking, dataset design, and monitoring.
  • Define abuse standards for novel AI use cases to effectively counter hyperscale threats.
  • Manage incident response and root cause analyses (RCAs) while partnering with teams to implement long-term security strategies.
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