Software Engineer 2 - Insider Risk

As organizations face increasingly sophisticated social engineering and insider threats, the very foundation of trust-the employee identity-is under attack. Abnormal's Identity Security team is building a groundbreaking product to detect and prevent fraudulent employee identities, specifically targeting high-stakes threats like infiltrators seeking to funnel funds through deceptive employment. We use advanced behavioral intelligence to scrutinize candidate details-from resumes and application metadata like IP addresses, email addresses, and phone numbers-to identify suspicious patterns and prevent malicious actors from entering the workforce. We are extending Abnormal's leadership in AI-native security to protect the integrity of the modern enterprise at the point of hire.

• Build identity verification and fraud detection systems to scrutinize candidate data during the application process.
• Develop sophisticated correlation engines that match candidate details (IPs, phone numbers, email history, resume metadata) against known indicators of fraudulent or state-sponsored activity.
• Create high-availability pipelines that ingest and analyze signals from application tracking systems (ATS), identity providers, and external risk intelligence.
• Ship automated guardrails that flag high-risk candidates in real-time, enabling security teams to act before an infiltrator is onboarded.
• Drive 01 iteration: prototype quickly, test fraud detection assumptions, learn from emerging threat patterns, and scale simple, effective solutions.
• Collaborate across security, platform, and data teams; write and review technical designs; and participate in core SDLC rituals.

• 2+ years building software applications.
• Experience productionizing large-scale, data-intensive systems.
• High velocity and creativity in solving technical challenges related to fraud detection and pattern matching.
• Experience & desire to adopt & improve AI-native development workflows.
• Strong debugging skills with logs, metrics, and behavioral signals.
• Ability to translate complex security and business requirements into high-quality software.
• Ability to independently solve complex problems and work cross-functionally.
• BS in CS/SE/IS or a related field.

• Experience with Go and Python.
• Experience in fraud detection, identity verification, or anti-money laundering (AML) systems.
• Background in cybersecurity, specifically focused on insider threats or nation-state actor TTPs (Tactics, Techniques, and Procedures).
• Experience with big data, statistics, and ML for identity/behavioral risk modeling and anomaly detection.

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