Sr. Security Software Engineer, Corporate Security

What you’ll do:

• Automation & Tooling - Develop scripts, tools, and automated pipelines to streamline vulnerability scanning, incident triage processes. Integrate security tools within CI/CD pipelines.
• Security Architecture & Code Review - Consult and engineer secure-by-design systems. Conduct design and code reviews to identify vulnerabilities, misconfigurations, and security flaws early in the development lifecycle.
• Champion Security Automation Culture: Advocate for and educate team members on secure coding, automation practices, and emerging security technologies.
• Implement Secure Engineering Practices - Design, develop, and maintain software systems with security best practices integrated throughout the development lifecycle.
• Collaborate with XFN Teams - Partner with security engineers and IT to improve detection and remediation of threats across infrastructure and applications.
• Use AI to accelerate analysis and iteration, while applying judgment and verification to ensure correctness and quality.
• Leverage AI to streamline and enhance the efficiency, accuracy, and coverage of security engineering and review processes.

What we’re looking for:

• Bachelor’s degree in Computer Science, Cybersecurity or, a related field or equivalent experience.
• 5+ years of experience in corporate security or security related software engineering role.
• Linux/UNIX, macOS or Windows internals with an emphasis on proactive hardening.
• Experience working in conjunction with IT architectural and infrastructure groups to coordinate and implement roadmaps for future scalability, growth, and capacity.
• Fleet management experience (e.g. Puppet, Chef, Terraform or similar).
• Cloud computing experience (infrastructure or security experience both valuable).
• Systems security experience (e.g. hardening a corporate identity environment).
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.
• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review).
• High integrity and ownership: you protect sensitive data, avoid over