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 0→1 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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