Senior Staff Software Engineer, Ai-empowered, Fraud and Abuse Security

Google Google · Big Tech · Sunnyvale, CA +3

Senior Staff Software Engineer role focused on architecting and scaling AI-empowered fraud and abuse security platforms within Google Cloud. The role involves designing and optimizing AI-driven security capabilities, establishing AI infrastructure standards, and leading technical direction for multiple teams at the intersection of security and ML. Requires experience in large-scale distributed systems and AI/ML infrastructure, with a focus on applying AI to security challenges.

What you'd actually do

  1. Architect and scale robust, secure platforms that automate security checks across Google Cloud’s developer pipeline and production systems.
  2. Design, optimize, and evaluate AI-driven security capabilities, leading the identification of AI opportunities across fraud and abuse engineering including direct coding and model architecture.
  3. Establish engineering and security standards for large-scale distributed systems, ensuring robust end-to-end security.
  4. Analyze and distill complex technical and security data to drive key organizational decisions and roadmap prioritization.
  5. Provide technical leadership for multiple teams, vet system designs, and mentor executive engineers to help them grow into technical leaders in AI and security.

Skills

Required

  • software development in one or more programming languages (e.g., Python, Go, C++, Java)
  • designing, building, and testing large-scale distributed systems or infrastructure services
  • AI/ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)
  • leading project teams, defining technical roadmaps, and setting engineering standards
  • designing and evaluating AI-assisted tools or automation workflows

Nice to have

  • anti-abuse, ad fraud detection, cybersecurity, or managing traffic quality systems
  • designing, optimizing, or evaluating Generative AI solutions, LLM-based applications, or multi-agent orchestration systems
  • building and deploying large-scale machine learning systems for fraud and abuse or security applications (e.g., fraud prevention or intrusion detection)
  • security assessment and design of global-scale distributed systems, with experience implementing end-to-end security controls (e.g., secure SDLC, automated vulnerability discovery, or threat analysis)
  • Google's security products, services, and underlying infrastructure

What the JD emphasized

  • AI/ML infrastructure
  • designing and evaluating AI-assisted tools or automation workflows
  • building and deploying large-scale machine learning systems for fraud and abuse or security applications
  • designing, optimizing, or evaluating Generative AI solutions, LLM-based applications, or multi-agent orchestration systems

Other signals

  • AI-enabled defense solutions
  • AI opportunities across fraud and abuse engineering
  • design and optimize AI-driven security capabilities
  • designing and evaluating AI-assisted tools or automation workflows
  • building and deploying large-scale machine learning systems for fraud and abuse or security applications