Software Engineer, Platform Security

Lead the application security strategy and implementation for Decagon AI's conversational platform that serves enterprise customers at scale. You'll partner with engineering teams to build security directly into our AI-powered applications, ensuring protection against application-layer threats while maintaining the performance and reliability our customers expect. This role offers the opportunity to apply deep application security expertise to AI systems and shape security practices across our rapidly growing engineering organization.

In this role, you will

Design and implement application security controls across our AI agent platform, including secure coding practices, threat modeling, and vulnerability management.

Collaborate closely with product engineering teams to integrate security throughout the software development lifecycle, from design, coding, PR, and deployment

Establish application security testing programs including static analysis (SAST), dynamic analysis (DAST), and interactive testing (IAST) tailored for AI applications

Lead security code reviews and architecture assessments for new features, with special focus on AI model integration points and customer data handling

Build security tooling and automation to enable developers to identify and remediate vulnerabilities quickly while maintaining development velocity

Respond to security incidents involving application vulnerabilities, coordinating remediation efforts and post-incident improvements

Your background looks something like this

Have 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

Even better

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

Compensation

$200K – $330K + Offers Equity