✦ Luna Orbit — Software Engineering

Software Engineer, Account Abuse

at Anthropic

📍 San Francisco, CA Hybrid Posted March 07, 2026
Type Full-Time
Experience mid
Exp. Years 5-10+ years
Education Bachelor's degree in Computer Science, Software Engineering or comparable experience
Category Software Engineering

This role involves building systems to analyze signals at scale to prevent abuse, fraud, and spam, ensuring fair resource allocation and system integrity.

  • Build signal analysis systems
  • Develop monitoring dashboards
  • Perform root cause analysis
  • Integrate with data vendors
  • Collaborate with data scientists

The technical environment includes Python, SQL, data analysis tools, and integration with third-party data vendors, focusing on scalable signal analysis and monitoring.

The ideal candidate is a mid-level software engineer with 5+ years of experience in Python, SQL, and data analysis tools, specializing in abuse, fraud, and spam detection systems. They possess strong analytical skills and experience building scalable signal analysis platforms.

PythonSQLData analysis tools5-10+ years experience in software engineeringAbility to analyze signals at scale
AI/ML systemsFraud-detection modelsSecurity monitoring toolsOperational tooling
PythonSQLThird-party data-enrichment vendors
PythonSQLData analysis toolsFraud detectionAbuse detectionMonitoring dashboardsRoot cause analysis
PythonSQLData analysis toolsIntegrity detectionSpam detectionFraud detectionAbuse detection
CommunicationProblem-solvingStakeholder collaborationAnalytical thinking
Industry Technology / AI & Machine Learning
Job Function Developing scalable abuse detection and signal analysis systems
Software EngineerPythonSQLData analysisFraud detectionSpam detectionAbuse detectionData-enrichmentMonitoring dashboardsRoot cause analysisSecurity monitoring toolsAI systemsML systemsIntegrity systemsStakeholder communicationThird-party data-enrichment

Less than 5 years of relevant experience, Lack of proficiency in Python and SQL, No experience with abuse or fraud detection

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