Software Engineer (Insider Risk)

Abnormal Security; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Healthcare

Flexible PTO

401k

One Medical

FSA

Mental Health Resources

Home Office Stipend

Internet & Phone Stipend

Wellness Stipend

Job summary

Abnormal Security is seeking a software engineer for its Identity Security team in San Francisco. You will build systems to verify candidate identities, detect fraud, and protect the hiring process from infiltrators with high-stakes threats.

The role emphasizes rapid prototyping, scalable data pipelines, and close collaboration with security, platform, and data teams to translate complex requirements into reliable, high-quality software.

Qualifications

• BS in CS/SE/IS or a related field.
• 2+ years building software applications.
• Experience with Go and Python.
• Experience productionizing large-scale, data-intensive systems.
• Experience with big data, statistics, and ML for identity/behavioral risk modeling and anomaly detection.
• Background in cybersecurity, insider threats or nation-state actor TTPs (Tactics, Techniques, and Procedures).
• Strong debugging skills with logs, metrics, and behavioral signals.
• Experience with AI-native development workflows.

Responsibilities

• Build identity verification and fraud detection systems to scrutinize candidate data during the application process
• Develop sophisticated correlation engines that match candidate details against indicators of fraudulent activity
• Create high-availability pipelines that ingest and analyze signals from ATS, identity providers, and external risk intelligence
• Ship automated guardrails that flag high-risk candidates in real-time for security teams
• Drive 0→1 iteration: prototype quickly, test fraud detection assumptions, and scale effective solutions
• Collaborate across security, platform, and data teams; write and review technical designs; participate in core SDLC rituals

Skills

2+ years software development

Go

Python

ML for identity risk

Big data

Anomaly detection

Security / fraud domain

Education

BS in CS/SE/IS or related field

Tools

Go

Python

Job description

• 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
• 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

Benefits

• Healthcare
• Flexible PTO
• 401k
• One Medical
• Flexible Spending Account
• Mental Health Resources
• Home Office Stipend
• Monthly Internet & Phone Stipend
• Health and Wellness Stipend

High velocity and creativity in solving technical challenges related to fraud detection and pattern matchingAbility to translate complex security and business requirements into high-quality softwareAbility to independently solve complex problems and work cross-functionallyBS in CS/SE/IS or a related fieldExperience & desire to adopt & improve AI-native development workflowsStrong debugging skills with logs, metrics, and behavioral signals2+ years building software applicationsExperience productionizing large-scale, data-intensive systemsExperience with big data, statistics, and ML for identity/behavioral risk modeling and anomaly detectionBackground in cybersecurity, specifically focused on insider threats or nation-state actor TTPs (Tactics, Techniques, and Procedures)Experience with Go and PythonExperience in fraud detection, identity verification, or anti-money laundering (AML) systems