Lead Engineer - Malware Reverse Engineering (cti Team)

Target Target · Retail · NCD-0375 Brooklyn Park, MN

Lead Engineer for Malware Reverse Engineering on the Cyber Fusion Center (CFC) team. This role focuses on investigating and implementing AI-assisted tooling to triage samples, validate machine-generated analysis, and investigate complex malware where automation falls short. The engineer will develop reverse engineering skills, analyze attacker tradecraft, and translate insights into durable detections. Responsibilities include AI-augmented malware analysis, triage at scale, targeted reverse engineering, exploit awareness, detection development, and tooling/pipeline interaction.

What you'd actually do

  1. Review and validate AI-generated static and dynamic analysis results.
  2. Analyze large sample sets and cluster malware into families and campaigns
  3. Perform focused reversing on critical code paths (i.e. loaders, unpacking routines, injection logic)
  4. Recognize common exploitation patterns (memory corruption, logic flaws, sandbox escapes)
  5. Contribute to high-quality detection logic (YARA, behavioral rules, heuristics)

Skills

Required

  • 4 year degree or equivalent experience
  • 7+ years of software or security engineering experience preferably in malware labs, CTFs or with personal research projects
  • Demonstrated understanding of reverse engineering concepts (x86/x64, assembly, calling conventions)
  • Familiarity with common malware techniques (packing, persistence, process injection)
  • Demonstrated programming knowledge in C/C++ and Python
  • Familiarity with YARA or other detection frameworks
  • Experience with tools like Ghidra, IDA Pro, Binary Ninja or similar
  • Exposure to dynamic analysis (debugging, sandboxing, instrumentation)
  • Understanding of OS internals (Windows or Linux), including processes, memory, and system calls
  • Basic networking knowledge (protocols, common attack surfaces)
  • Ability to reason about unfamiliar code and derive behavior from partial information
  • Basic knowledge of exploitation concepts (i.e. buffer overflows, ROP)
  • Curiosity when things don’t match expectations—willingness to dig deeper and analyze
  • Comfort working with incomplete or noisy data at scale
  • Willingness to rely on automation without blindly trusting it
  • Ability to critically evaluate machine-generated analysis
  • Interest in how adversaries may evade or manipulate automated systems
  • Maintains technical knowledge within areas of expertise
  • Stays current with new and evolving technologies via formal training and self-directed education

What the JD emphasized

  • AI-assisted tooling
  • validate machine-generated analysis
  • investigate complex or evasive malware where regular automation falls short
  • Ability to critically evaluate machine-generated analysis
  • Interest in how adversaries may evade or manipulate automated systems

Other signals

  • AI-assisted tooling
  • validate machine-generated analysis
  • refine analysis by guiding tools
  • work with automated analysis pipelines
  • leverage Python scripting to extend or customize tooling