Aaron Fine

Systems engineering grounded in building real software.

I’m a senior systems engineer with a software engineering background. I work at the boundary between architecture, implementation, evidence, and technical decision-making—especially where complex software must remain understandable to the people responsible for it.

9 years
At Space Dynamics Laboratory
B.S. Computer Science
Utah State University
6 patents
Issued U.S. patents
Software → systems
Engineering and technical leadership

Professional work

From implementation to technical leadership

I joined Space Dynamics Laboratory as a student, completed its Student Scholar program, and joined the full-time staff as a software engineer after earning my computer science degree in 2019. I now provide systems engineering and technical leadership for software-intensive national-security work.

My current work centers on technical-baseline stewardship, independent software evaluation, design-review support, and translating technical evidence into useful recommendations for government and industry teams. The recurring questions are practical ones: Can the delivered system be built and operated? Does its behavior match its documentation? Are interfaces, assumptions, risks, and technical debt understood well enough to make responsible decisions?

Earlier roles kept me close to implementation. I developed simulation, visualization, networking, logging, state-management, and control software using C++, Qt, MATLAB, containers, and CI/CD. That background continues to shape how I approach systems engineering: architecture claims should eventually meet testable, maintainable code, configuration, documentation, data, logs, operators, and failure. Observability is part of the system, not an afterthought.

Current focus

Work that stays understandable

My interests converge on systems whose authority, assumptions, interfaces, and failure modes can be examined rather than merely trusted.

Assurance

Software that can be examined

Technical baselines, evidence-driven evaluation, meaningful documentation, observable behavior, and actionable findings.

Evaluation method →
Engineering breadth

Hardware, software, and operations

Product development, simulation, networking, embedded interfaces, containers, infrastructure, manufacturing, and technical writing.

Patents and earlier work →
AI-assisted work

Accountable automation

Explicit control boundaries, durable context, human approval, inspectable artifacts, and evaluation beyond fluent output.

Morning Briefing →