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Software Engineer II - Insider Risk

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Software Engineer II - Insider Risk in USA

  • Abnormal Security
  • Full time
  • Email
  • 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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