Reverse Engineer & AI Workflow Architect

The Johns Hopkins University Applied Physics Laboratory; Laurel (MD); On-site; USD 100,000 - 245,000; Full time

Job summary

The Johns Hopkins University Applied Physics Laboratory is seeking experienced reverse engineers and AI-enabled developers in Laurel, MD. You will investigate complex systems, develop AI-enabled workflows, and design infrastructure to accelerate cyber analysis and engineering tasks.

Ideal candidates have a Bachelor's in a relevant field, 2+ years in reverse engineering, and hands-on experience with AI tools and low-level analysis.

Qualifications

• Bachelor's degree in a relevant technical field.
• 2+ years in reverse engineering, software/cyber analysis, or AI-enabled development.
• Experience with Ghidra, IDA Pro, Binary Ninja or similar tools.
• Strong OS, architecture, and software internals knowledge.
• Proficient in Python, C/C++, Rust or similar.

Responsibilities

• Reverse engineer software, firmware, and hardware to understand system behavior.
• Develop agentic AI workflows to augment reverse-engineering tasks.
• Design AI agents to interface with tools and data sources for analyses.
• Build and maintain AI development infrastructure and MCP services.
• Evaluate AI models, prompts, tools, and workflows for reliability.
• Test, document, and integrate capabilities into sponsor environments.

Education

Bachelor's degree

Master's degree

PhD (preferred)

Reverse engineering

Python

C/C++

Rust

Strong communication

Security clearance eligible

Tools

Ghidra

IDA Pro

Binary Ninja

debuggers

disassemblers

Job description

The Johns Hopkins University Applied Physics Laboratory is seeking experienced reverse engineers and AI-enabled developers in Laurel, MD. You will investigate complex systems, develop AI-enabled workflows, and design infrastructure to accelerate cyber analysis and engineering tasks.

Ideal candidates have a Bachelor's in a relevant field, 2+ years in reverse engineering, and hands-on experience with AI tools and low-level analysis.