About the Role 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. What you will do 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. Must Haves 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. Nice to Have 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. #LI-NT1 Actual compensation will be determined based on several non-discriminatory factors including skills, experience, qualifications, and geographic location. In addition to base salary, this role may be eligible for bonus or incentive compensation, equity, and a comprehensive benefits package. Base salary range: $149,200 — $214,500 USD A note on AI in our process: Abnormal AI uses AI-assisted tools to help our recruiting team prepare for candidate interviews. These tools analyze resume content and role requirements to suggest interview questions and areas for the interviewer to explore.They do not make hiring decisions or screen candidates automatically. Every decision about a candidacy is made by a person. Further, if your application is successful and Abnormal AI makes a conditional offer of employment, we will carry out pre-employment checks which must be successfully completed to progress to a final offer. All processes and pre-employment checks are in line with prevailing legislation and Abnormal AI's policies relevant to our security and privacy standards. Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here . If you would like more information on your EEO rights under the law, please click here .
