Jonathan Zuramski

Jonathan Zuramski

Security Engineer at Meta — Malware & App Integrity

Jonathan is a Security Engineer at Meta, working on the Malware & App Integrity team. He holds a degree from the University of Maryland College Park, in Computer Science. He currently focuses on building scalable systems for malware analysis and threat intelligence, with a particular emphasis on automating the reverse engineering pipeline and integrating AI tooling into security workflows.

  • Security Engineer at Meta
  • UMD — Computer Science
  • Malware Reverse Engineering
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Skills

A mix of defensive security depth, software engineering breadth, and modern AI tooling.

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Malware Reverse Engineering

Analyzing and dissecting malicious software to understand behavior, unpack obfuscation, and extract indicators of compromise from real-world threats.

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Software Engineering

Building production systems in Python and beyond — from internal security tooling and automation pipelines to full-stack data enrichment platforms.

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AI Tooling

Integrating LLMs and AI APIs into practical workflows — from automated threat classification to building and teaching hands-on AI applications.

Projects

Selected security engineering work built at scale.

Threat Intelligence Platform

Security Engineering

Designed and built a threat intelligence platform that aggregates data from multiple security tools to enrich indicators of compromise (IOCs) for an internal threat feed. The system provides actionable classifications for each IOC along with confidence and severity scores, and includes automated false-positive filtering to reduce analyst noise. By centralizing enrichment across disparate data sources, the platform significantly improves the speed and reliability of threat triage.

Automated Unpackers

Malware Analysis

Developed a suite of automated unpackers capable of handling multiple custom packer and obfuscation techniques found in real-world malware. Rather than relying on manual, sample-by-sample analysis, the system identifies packing signatures and applies the appropriate unpacking routine automatically — recovering the underlying payloads at scale. This significantly accelerates the reverse engineering pipeline and enables downstream analysis tools to operate on clean, unpacked binaries.