Abnormal AI is focused on enhancing security through innovative identity verification solutions. The Software Engineer 2 will develop systems to detect and prevent fraudulent employee identities, utilizing advanced behavioral intelligence and collaborating with various teams to ensure the integrity of the hiring process.
Responsibilities:
- 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
Requirements:
- 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