Lead Application Security Engineer

About this role

WHO WE ARE

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com .

About the Role We’re seeking a Lead Application Security Engineer to help advance Zeta Global’s application and platform security posture through AI-native security practices, intelligent automation, and scalable security engineering. You’ll play a critical role in embedding security throughout the software development lifecycle by using AI-driven tools, automated controls, and data-informed risk prioritization to ensure our systems, applications, and AI-powered platforms are built securely from the ground up.

Zeta operates at massive scale, powering billions of consumer profiles and petabytes of data across real-time, AI-powered marketing platforms. In this role, you’ll collaborate with Engineering, Product, QA, DevOps, and AI platform teams to identify risks, design secure-by-default patterns, and build automated security capabilities that enable secure innovation at speed.

This position offers significant technical scope, cross-functional visibility, and the opportunity to directly influence the company’s security maturity through AI-enabled threat modeling, automated validation, intelligent vulnerability management, and proactive defense.

Key Responsibilities

AI-Driven Threat Modeling & Security Validation

• Use AI-assisted threat modeling capabilities to identify application, platform, API, cloud, data, and AI/ML security risks early in the design and development process.

• Leverage automated security review tools to evaluate architecture, design documents, code changes, APIs, and data flows for security gaps and control weaknesses.

• Drive AI-assisted code security reviews using SAST, DAST, SCA, secrets detection, IaC scanning, container scanning, and contextual risk analysis.

• Use automation and intelligent correlation to assess third-party libraries, APIs, vendor integrations, and open-source dependencies for security, compliance, and supply-chain risk.

• Support AI-enabled red team, blue team, and incident response simulations to validate detection, prevention, and response capabilities.

Embedding AI-Native Security into the SDLC

• Partner with developers and QA engineers to embed AI-driven security testing and automated risk detection into CI/CD pipelines.

• Build and improve security automation that provides real-time feedback to developers during design, coding, testing, release, and deployment.

• Use AI-assisted analysis to review architecture and design artifacts, identify risks earlier, and recommend secure implementation patterns.

• Contribute to intelligent security checkpoints that reduce manual review effort while improving consistency, traceability, and developer velocity.

• Help design scalable guardrails, reusable security controls, and policy-as-code capabilities across application and platform teams.

Emerging Threat Monitoring & Proactive Defense

• Monitor evolving application, cloud, API, AI/ML, and data security risks using AI-assisted threat intelligence, vulnerability intelligence, and attack-pattern analysis.

• Identify and evaluate AI-specific threats such as prompt injection, data poisoning, model abuse, model leakage, insecure tool use, and sensitive data exposure.

• Assist in designing and deploying proactive defense mechanisms across applications, APIs, data platforms, and AI-powered systems.

• Use automated signals, telemetry, and risk scoring to support investigations, post-incident analysis, and continuous improvement of prevention and detection capabilities.

• Translate recurring vulnerabilities and incidents into feedback loops that improve threat models, secure design patterns, and SDLC controls.

Security Awareness, Standards & Scalable Enablement

• Promote secure coding and secure design practices through AI-assisted guidance, reusable playbooks, automated recommendations, and developer-friendly documentation.

• Contribute to internal security standards, secure engineering patterns, and AI-native security playbooks.

• Help teams adopt security self-service capabilities that reduce dependency on manual AppSec review.

• Collaborate closely with Engineering, DevOps, QA, Product, and AI platform teams to foster a security-first and automation-first culture.

• Use metrics and insights to measure control effectiveness, remediation trends, developer adoption, and overall security maturity.

What You Need to Succeed

• Bachelor’s degree in Computer Science, Cybersecurity, or a related field, or equivalent practical experience.

• 5+ years of experience in Application Security, DevSecOps, Secure Software Development, or Security Engineering.

• Strong understanding of OWASP Top 10, SANS CWE Top 25, secure design principles, and application threat modeling.

• Familiarity with AI/ML security concepts such as prompt injection, data poisoning, adversarial testing, model integrity, model abuse, and AI supply-chain risks.

• Experience building or integrating AI-assisted security workflows, security bots, automated triage systems, or risk scoring models.

• Experience using AI-assisted or automation-driven approaches to improve security testing, vulnerability analysis, c

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