Cloud AI Protection Manager
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- 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.
- 7 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
- 7 years of experience managing projects and defining project scope, goals, and deliverables.
Preferred qualifications:
- Master’s degree or PhD in Computer Science, AI, Machine Learning, or related technical field.
- 10 years of experience in Trust and Safety, cybersecurity, or fraud detection, including 3 years managing technical teams.
- Experience designing machine learning classifiers, neural networks, and automated red-teaming/vulnerability pipelines.
- Experience influencing senior cross-functional stakeholders across Engineering, Product, Research, and Legal to implement large-scale defense-in-depth protections.
- Expertise in Generative AI, LLMs, and autonomous agent threat landscapes, including adversarial risks like jailbreaking and data exfiltration.
About the job
As a part of the Trust and Safety Cloud AI team, the vanguard of Cloud AI security and safety, your mission is to safeguard users, protect the integrity of our platforms, and ensure the responsible deployment of AI products at global scale. You will operate at the critical intersection of cutting-edge AI research and real-world security, building the foundational defenses that prevent the misuse of generative models and agents. You will pioneer proactive threat detection and robust mitigation strategies, empower Google Cloud to push the boundaries of AI innovation safely, ethically, and securely.As a part of the Engineering Analyst Manager for Cloud AI Security and Abuse, you will lead a high-performing team protecting Google Cloud’s cutting-edge AI products and developer platforms (including Vertex AI, Gemini Enterprise, AI Studio, and Cloud APIs) against sophisticated adversarial actors and emerging frontier risks. You will sit at the intersection of data science, security engineering, and threat intelligence, your team uncovers complex abuse patterns across billions of events, architects automated detection and enforcement pipelines, and builds next-generation guardrails for generative and agentic AI systems.
You will combine people leadership with deep technical and analytical acumen, mentoring Engineering Analysts, guiding the development of AI-driven defenses, and partnering closely with leaders across Cloud, DeepMind, Security, Product, and Policy. Your leadership will ensure that Google’s enterprise and developer AI deployments remain resilient against exploitation while balancing rigorous protection with a friction-less customer experience.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $188000 - $274000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Mentor and grow a high-performing team of Engineering Analysts to foster a culture of technical excellence and innovation.
- Define the strategic vision, Objectives and Key Results (OKRs), and roadmaps for Cloud AI security, balancing urgent incident response with foundational, long-term defenses.
- Drive the design and deployment of scaled detection systems, including ML, Large Language Model (LLMs), and heuristic frameworks to mitigate threats like prompt injection and abuse.
- Advance proactive threat intelligence, adversarial benchmarking, and automated red-teaming to identify novel attack vectors in agentic AI.
- Partner cross-functionally across Engineering, Research, and Legal to translate security insights into hardened architectures, while establishing rigorous measurement frameworks to ensure operational precision for enterprise AI requests.
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