Senior Software Engineer, Ai-empowered Security, Cloud Ciso

Google Google · Big Tech · Sunnyvale, CA +3

Senior Software Engineer role focused on AI-empowered security for Google Cloud. The role involves architecting and scaling platforms that automate security checks, designing and evaluating AI-driven security capabilities, and leading identification of AI opportunities. It requires experience in security engineering, AI/ML infrastructure, and designing AI-assisted tools, with preferred experience in Generative AI, LLMs, and multi-agent systems.

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 CCSE; including coding.
  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.

Skills

Required

  • Python or C++
  • software design and architecture
  • security engineering
  • infrastructure security
  • secure software development lifecycle (SDLC)
  • threat modeling
  • automated vulnerability discovery
  • AI/ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data processing
  • debugging
  • designing and evaluating AI-assisted tools
  • automation workflows

Nice to have

  • Master's degree or PhD in Computer Science or related technical field
  • data structures and algorithms
  • technical leadership role
  • Generative AI solutions
  • LLM-based applications
  • multi-agent orchestration systems
  • synthesizing large-scale telemetry
  • security data
  • system logs
  • systemic gaps
  • organizational objectives
  • security assessment and design of global-scale distributed systems
  • implementing end-to-end security controls

What the JD emphasized

  • AI/ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)
  • AI-assisted tools or automation workflows
  • Generative AI solutions, LLM-based applications, or multi-agent orchestration systems

Other signals

  • AI-driven security capabilities
  • automate security checks
  • novel vulnerabilities
  • automate fixing issues
  • AI-assisted tools