Sr Engineer - Malware Reverse Engineering (ai-augmented Analysis)

Target · Retail · NCD-0375 Brooklyn Park, MN

Senior Engineer focused on Malware Reverse Engineering, leveraging AI-augmented analysis to triage samples, validate machine-generated analysis, and investigate complex malware. The role involves developing reverse engineering skills, analyzing attacker tradecraft, and translating insights into durable detections. Responsibilities include reviewing AI analysis results, identifying inaccuracies, refining analysis, triaging large sample sets, performing targeted reverse engineering on critical code paths, analyzing obfuscation techniques, understanding exploit patterns, and contributing to detection logic development using tools like YARA. The role also involves working with automated analysis pipelines and using Python scripting to enhance tooling.

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
  • 5+ 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
  • critical code paths
  • obfuscation, packing and anti-analysis techniques
  • automated analysis pipelines

Other signals

  • AI-assisted tooling
  • AI-generated static and dynamic analysis
  • validate machine-generated analysis
  • improve analysis workflows and signal quality
  • Leverage Python scripting to extend or customize tooling