Staff Software Engineer - Platform Security

ServiceNow•San Diego, CA

13h•$149,800 - $262,200

About The Position

ServiceNow’s Platform Security team builds foundational security infrastructure and AI-driven detection systems for enterprise-scale operations. The Outbound Security team owns controls for traffic leaving the ServiceNow platform, including egress filtering and policy, outbound proxying, SSRF protection, data exfiltration prevention, and workload egress identity for a SaaS platform. The Staff AI Security & Outbound Security Engineer will architect and deliver enterprise-scale security solutions integrating outbound security systems with machine learning, reasoning engines, and real-time inference. The role applies deep expertise in network and application egress security, technical leadership, and hands-on machine learning to design and operate adaptive security systems for egress control, outbound proxy infrastructure, and exfiltration detection at SaaS scale. The work supports FIPS-compliant and 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 operating services on Kubernetes in a SaaS or multi-tenant environment, including deployments, Helm, and CI/CD.
Working understanding of TCP/IP, DNS, HTTP, TLS/PKI fundamentals, proxies, firewalls, and allowlisting and denylisting.
Demonstrated security-first engineering practices, including secure coding, threat modeling, and disciplined code review.

Nice To Haves

Direct experience in outbound or egress security, forward proxies, SSRF mitigation, DNS security, or data loss and exfiltration prevention, including egress gateways, network policy enforcement, or TLS inspection.
Experience with authentication and authorization, IAM, certificates, identity, secure connectivity, and external network topology.
Hands-on experience applying machine-learning 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, or Common Criteria.
Exposure to post-quantum cryptography for outbound TLS and secure communications.
A track record of mentoring engineers and raising a team's security standards.

Responsibilities

Design, build, and maintain 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 coverage, and incident response.
Apply threat modeling and security considerations to design reviews and feature development.
Evaluate appropriate uses of AI and machine learning in outbound security, including egress 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 risks such as SSRF, DNS rebinding, and open redirects, unrestricted egress, and rigorous design reviews.
Monitor evolving exfiltration techniques and egress-security advancements, including post-quantum readiness for outbound TLS, and bring relevant considerations forward ahead of compliance deadlines.