Platform Engineer, Security

Decagon Decagon · Vertical AI · San Francisco, CA · Engineering

This role is for a Platform Engineer focused on Security within a conversational AI company. The primary responsibility is to lead the application security strategy and implementation for the AI platform, ensuring protection against threats while maintaining performance. This involves designing controls, collaborating with engineering teams, establishing testing programs, conducting code reviews, building security tooling, and responding to incidents. While the role operates within an AI company and interacts with AI systems, the core craft is application security engineering, not building or researching AI models themselves.

What you'd actually do

  1. Design and implement application security controls across our AI agent platform, including secure coding practices, threat modeling, and vulnerability management.
  2. Collaborate closely with product engineering teams to integrate security throughout the software development lifecycle, from design, coding, PR, and deployment
  3. Establish application security testing programs including static analysis (SAST), dynamic analysis (DAST), and interactive testing (IAST) tailored for AI applications
  4. Lead security code reviews and architecture assessments for new features, with special focus on AI model integration points and customer data handling
  5. Build security tooling and automation to enable developers to identify and remediate vulnerabilities quickly while maintaining development velocity
  6. Respond to security incidents involving application vulnerabilities, coordinating remediation efforts and post-incident improvements

Skills

Required

  • 3-5 years of hands-on application security engineering experience
  • Expertise in secure software development practices, including threat modeling, secure code review, and vulnerability assessment
  • Strong software engineering background with ability to review code across multiple languages and frameworks commonly used in AI/ML applications
  • Experience implementing application security testing tools and integrating security into CI/CD pipelines
  • Knowledge of OWASP Top 10, common application vulnerabilities, and modern application security frameworks
  • Proven track record working with engineering teams to remediate security findings while balancing security and business requirements

Nice to have

  • Experience securing AI/ML applications, including prompt injection, model extraction, and adversarial input protections
  • Background with large-scale, multi-tenant SaaS applications handling sensitive customer data
  • Familiarity with Google Cloud application security services and container security best practices
  • Knowledge of enterprise compliance requirements (SOC 2, ISO 27001, GDPR) from an application security perspective
  • Experience with modern security tools like Semgrep, CodeQL, Cursor Bug Bot, XBOW, or similar

What the JD emphasized

  • application security strategy
  • AI agent platform
  • AI applications
  • AI model integration points
  • customer data handling
  • secure coding practices
  • threat modeling
  • vulnerability management
  • application security testing
  • security code reviews
  • architecture assessments
  • security tooling and automation
  • security incidents
  • application vulnerabilities
  • remediate security findings
  • balancing security and business requirements
  • securing AI/ML applications
  • prompt injection
  • model extraction
  • adversarial input protections
  • large-scale, multi-tenant SaaS applications
  • sensitive customer data
  • enterprise compliance requirements
  • SOC 2
  • ISO 27001
  • GDPR