Senior Security Engineer, Incident Response

Responsibilities

• Lead and support the response to all security events and incidents across Twilio’s complex global infrastructure, services and applications.
• Document incidents and projects, and craft best practices as runbooks and standard operating procedures.
• Work cross-collaboratively to understand and help solve challenges related to a broad spectrum of threat actors and activity.
• Improve Twilio’s security and reliability posture by driving identified betterments from security events and incidents.
• Rapidly acquire new technical skills and knowledge in a fast-paced, highly disruptive industry environment.
• Own the security incident lifecycle, respond to incidents, participate in on-call rotation and RCAs for security incidents.
• Build, cultivate, and maintain positive relationships with internal customers to identify and facilitate solutions.
• Provide mentorship, support, and care for the team to enable long-term career development.

Qualifications

Required:

• 5+ years of security incident response in a production-cloud environment.
• Subject-matter expert on security issues and technologies.
• Ability to utilize AI for comprehensive, complex security incident response activities delivering high fidelity detections.
• Advanced knowledge of service-oriented architectures, and experience with security tools and technologies fit for a cloud environment.
• Experience working across a technology stack on difficult security challenges and initiatives.
• Experience with SIEM platforms and the ability to extend their functionality.
• Experience with SOAR tools and automating manual security processes.
• Experience in either AWS, GCP, or other large cloud platform.
• Excellent written and verbal communication skills.
• Ability to influence and build effective working relationships with every level of the organization.

Desired:

• AI Model Security & Posture Management: Support implementation of controls and safeguards to prevent AI vulnerabilities, such as prompt injections, model evasion, and data poisoning.
• AI-Driven Threat Response: Utilize GenAI and LLM-based security use cases to rapidly interpret alerts, reduce false positives, and contextualize threat data.
• Artifact & Evidence Triage: Collect intrusion artifacts and forensically sound images to analyze malware, trojans, and source code.
• Threat Intelligence Integration: Monitor external vendor data and threat intelligence to analyze trends and proactively defend against emerging risks.