Drimva

Two Six Technologies

Embedded Systems Reverse Engineer

$112K–$168K

Herndon, Virginia +1 · Full-time · Posted Sep 2, 2026

Location
Herndon, Virginia
13865 Sunrise Valley Dr Herndon, VA
Education
Bachelor’s (or higher) degree in Computer Science, Computer/Electrical Engineering, or a related field (or equivalent practical experience).

About the job

Reverse engineer embedded and wireless systems to identify vulnerabilities, develop proof-of-concept exploits, and use agentic AI to accelerate security research.

What you'll do

  • Reverse engineer network devices and embedded systems, analyze firmware and protocols, and assess vulnerabilities.
  • Develop maintainable proof-of-concept exploits and custom binary analysis tools.
  • Integrate agentic AI systems into firmware red-teaming, vulnerability discovery, and reverse-engineering workflows.

What they ask for

  • Bachelor’s (or higher) degree in Computer Science, Computer/Electrical Engineering, or a related field (or equivalent practical experience).
  • Experience developing, debugging, and scripting in C/C++ and Python in Linux command-line environments, with low-level programming, memory management, and system interaction.
  • Experience with reverse engineering and vulnerability research using tools such as IDA Pro, Binary Ninja, or Ghidra.
  • Experience or familiarity with agentic AI for red-teaming, vulnerability research, or hardware/firmware analysis; expertise in at least one listed specialty area; ability to work daily in the Herndon office; active Top Secret clearance and ability to obtain and maintain TS/SCI.

Nice to have

  • AI-assisted security paradigms; embedded development using C/C++ for RTOS or Linux.
  • Cryptographic security, secure boot, hardware debugging (JTAG/SWD), SDR, signal processing, side-channel analysis, or client-facing technical experience.

Pay

$111,983 — $167,974 USD (base salary)

Tools

C/C++, Python, Linux, IDA Pro, Binary Ninja, Ghidra, QEMU, ARM, MIPS, PowerPC

AI focus

Integrate agentic AI systems, including automated exploit synthesis tools, LLM-driven fuzzers, or autonomous security agents, to accelerate firmware red-teaming, vulnerability discovery, and reverse engineering.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 4, 2026 (UTC)