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.
- 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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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.
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.
Skills Required
- 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 and desire to adopt and 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
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field
- Experience with Go and Python
- Experience in fraud detection, identity verification, or anti-money laundering systems
- Cybersecurity background focused on insider threats or nation-state actor tactics, techniques, and procedures
- Experience with big data, statistics, and machine learning for identity or behavioral risk modeling and anomaly detection
Abnormal Security Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Abnormal Security and has not been reviewed or approved by Abnormal Security.
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Fair & Transparent Compensation — Pay is considered aggressively benchmarked to leading tech markets with annual reviews, and feedback suggests engineering and sales roles are compensated competitively with strong upside potential.
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Healthcare Strength — Health coverage is portrayed as robust, including employer-paid premiums for employees in prior postings, One Medical access, and globally designed healthcare and parental leave.
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Leave & Time Off Breadth — Time off provisions include flexible/unlimited PTO, company holidays, and paid parental leave that the company positions as globally available.
Abnormal Security Insights
What We Do
The Abnormal Security platform protects enterprises from targeted email attacks. Abnormal Behavior Technology (ABX) models the identity of both employees and external senders, profiles relationships and analyzes email content to stop attacks that lead to account takeover, financial damage and organizational mistrust. Though one-click, API-based Office 365 and G Suite integration, Abnormal sets up in minutes and does not disrupt email flow. Abnormal Security was founded in 2018 by CEO Evan Reiser, CTO Sanjay Jeyakumar, Head of Machine Learning Jeshua Bratman, and Founding Engineers Abhijit Bagri and Dmitry Chechik. The team previously built behavioral profiling and machine learning technologies at Twitter, Google and Pinterest that are being applied to solve a problem that costs organizations $1 billion per year, according to the FBI. The Abnormal Security platform stops targeted phishing, business email compromise and account takeover attacks that have never been seen before.
