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Systems Security Software Engineer

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At a Glance

Location
Seattle, Washington, United States
Experience
7+ years
Posted
2026-07-29T22:23:05-04:00

Key Requirements

Required Skills

CI/CDGitLinuxPython

Domain Knowledge

  • Automation
  • Embedded Systems
  • Engineering

Requirements

7+ years of professional experience developing systems software using C and/or C++.

5+ years of experience working with LLVM, compiler infrastructure, compiler development, or program analysis.

3–5+ years of experience with fuzzing, vulnerability research, or application security.

Experience developing compiler passes, static analysis tools, or code transformation frameworks.

Strong understanding of LLVM IR and compiler optimization concepts.

Experience debugging low-level software, memory corruption issues, and complex system behavior.

Compensation & Benefits

At Blueprint, we strive to offer competitive pay that reflects the value of our team members. Compensation for this role is influenced by a variety of factors, including skills, education, responsibilities, experience, and geographic market. For candidates based in Washington State, the anticipated salary range is

$65 to 75/hr.

Please note that we typically do not hire new employees at the top of the posted range. Actual starting pay will be determined based on experience, skills, and internal equity. The final salary and job title may vary depending on the selected candidate’s qualifications and could fall outside the stated range.

Responsibilities

Design, develop, and maintain automated firmware security analysis pipelines for vulnerability discovery.

Build low-level tooling using C++, Python, and LLVM to analyze firmware at scale.

Develop compiler passes and transformations to convert firmware into analyzable LLVM Intermediate Representation (IR).

Create, improve, and maintain fuzzing harnesses to maximize code coverage and uncover security vulnerabilities.

Investigate crashes identified during fuzzing campaigns and perform root-cause analysis.

Improve vulnerability detection accuracy by reducing false positives and enhancing automated analysis workflows.