Staff Software Engineer - Platform Security

ServiceNow•San Diego, CA

15h•$149,800 - $262,200

About The Position

The Staff AI Security & Outbound Security Engineer will architect and deliver enterprise-scale security solutions that integrate outbound security systems with machine learning, reasoning engines, and real-time inference. The role brings together expertise in network and application egress security, technical leadership, and hands-on machine learning to design and operate intelligent, adaptive security systems for egress control, outbound proxy infrastructure, and exfiltration detection at SaaS scale. The Platform Security Team builds foundational security infrastructure and AI-driven detection systems for enterprise-scale operations. Outbound Security owns controls on traffic leaving the ServiceNow platform across multiple domains, including egress filtering and policy, outbound proxying, SSRF protection, data exfiltration prevention, and workload egress identity. The work supports a SaaS platform in regulated environments.

Requirements

At least 8 years of professional software engineering experience.
Production-scale experience with Java, Go, or a comparable strongly typed language.
Strong computer science fundamentals, including data structures, algorithms, object-oriented design, and distributed systems tradeoffs.
Production experience running services on Kubernetes, including deployments, Helm, and CI/CD, in a SaaS or multi-tenant environment.
Working understanding of core network and application security concepts, including TCP/IP, DNS, HTTP, TLS/PKI fundamentals, proxies, firewalls, and allowlisting/denylisting.
Demonstrated security-first engineering habits, including secure coding practices, threat modeling, and code review discipline.

Nice To Haves

Direct experience in outbound or egress security, forward proxies, SSRF mitigation, DNS security, or data loss/exfiltration prevention, including egress gateways, network policy enforcement, or TLS inspection.
Experience with AuthN/AuthZ, IAM, certificates, identity, secure connectivity, and external network topology.
Hands-on AI/ML experience applying techniques to security or operations problems, such as anomaly detection, risk scoring, or log and telemetry analysis, or critically integrating LLM tooling into engineering workflows.
Experience in regulated environments, such as FIPS 140-2/3, FedRAMP, Common Criteria, or similar.
Exposure to post-quantum cryptography in the context of outbound TLS and secure communications.
Track record of mentoring engineers and raising a team's security bar.

Responsibilities

Design, build, and maintain high-quality, scalable, secure outbound security services that meet FIPS compliance and regulated-environment requirements.
Architect and operate mission-critical egress services with reliability and security as first-class design constraints.
Own services end-to-end on Kubernetes in a multi-tenant SaaS environment, including deployment, scaling, on-call duties, and incident response.
Apply a security mindset to design reviews by threat-modeling features beyond their happy paths.
Evaluate appropriate uses of AI and machine learning in outbound security, including egress traffic anomaly detection, destination risk scoring, and AI-assisted code review for SSRF and unsafe outbound-request patterns.
Collaborate with product owners, security architects, and platform teams to translate compliance and security requirements into outbound security frameworks.
Mentor engineers on secure coding, outbound-request pitfalls such as SSRF, DNS rebinding, open redirects, and unrestricted egress, and rigorous design reviews.
Stay current on exfiltration techniques and egress-security advances, including post-quantum readiness for outbound TLS, and bring relevant considerations forward ahead of compliance deadlines.