Software Engineer II - Insider Risk

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Software Engineer II - Insider Risk in USA

Abnormal Security
Full time
Email

USA
09/12/2026
Report

San Francisco, CA

Responsibilities

Build identity verification and fraud detection systems for candidate application data.
Develop correlation engines matching IPs, phone numbers, email history, resume metadata, and other indicators against known fraudulent or state-sponsored activity.
Create high-availability pipelines ingesting signals from application tracking systems, identity providers, and external risk intelligence.
Ship real-time automated guardrails that flag high-risk candidates before onboarding.
Prototype and scale fraud detection solutions based on emerging threat patterns.
Write and review technical designs, debug systems using logs and metrics, and collaborate across security, platform, and data teams.

Requirements

At least 2 years of experience building software applications.
Experience productionizing large-scale, data-intensive systems.
Ability to solve fraud detection and pattern-matching challenges with high velocity and creativity.
Experience with or interest in improving 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.
Preferred experience with Go and Python, fraud detection, identity verification, anti-money laundering systems, cybersecurity, insider threats, nation-state actor TTPs, big data, statistics, or machine learning.

Benefits

Base salary range of $149,200-$214,500 USD.
May be eligible for bonus or incentive compensation, equity, and a comprehensive benefits package.

Salary: $149k - $215k/yr

Abnormal Security

Abnormal AI is the leading AI-native human behavior security platform, leveraging machine learning to stop sophisticated inbound attacks and detect compromised accounts across email and connected applications.

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