Senior Software Engineer - Insider Risk

Abnormal•San Francisco, CA

2d•$179,800 - $258,500

About The Position

Abnormal’s Identity Security team is building a product to detect and prevent fraudulent employee identities, including high-stakes threats such as infiltrators seeking to funnel funds through deceptive employment. The team uses behavioral intelligence to examine candidate details—including resumes and application metadata such as IP addresses, email addresses, and phone numbers—to identify suspicious patterns and prevent malicious actors from entering the workforce. The product extends Abnormal’s AI-native security capabilities to protect enterprise integrity at the point of hire. The Senior Software Engineer will build identity verification and fraud detection systems, develop correlation engines for candidate information and known fraud indicators, and create high-availability pipelines that analyze signals from applicant tracking systems, identity providers, and external risk intelligence. The role also involves shipping real-time automated guardrails, rapidly testing and iterating on threat-detection approaches, collaborating across security, platform, and data teams, writing and reviewing technical designs, and participating in core software development lifecycle activities.

Requirements

5+ years of experience 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 with and desire to adopt and improve AI-native development workflows.
Strong debugging skills using 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 computer science, software engineering, information systems, or a related field.

Nice To Haves

Experience with Go and Python.
Experience in fraud detection, identity verification, or anti-money laundering (AML) systems.
Cybersecurity background, particularly focused on insider threats or nation-state actor tactics, techniques, and procedures (TTPs).
Experience with big data, statistics, and machine learning for identity or behavioral risk modeling and anomaly detection.

Responsibilities

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