KD

Kai Dyer is a software engineer building tools for modeling, simulation, and analysis.

See selected work GitHub

About

Thoughtful software, built for real people and real constraints.

My work sits at the intersection of product thinking and technical depth. I enjoy taking dense problems, finding the signal, and turning them into tools that teams can actually trust and use.

That has led me from app development to machine learning experiments to systems modeling, always with the same goal: build something dependable, understandable, and genuinely useful.

Web development iOS apps Simulation Systems engineering Machine learning

Experience

Shipping analytical software for complex engineering teams.

Software Engineer

The MITRE Corporation

June 2023 - Present

I design and build engineering tools that help teams validate models, analyze complex systems, and communicate technical results clearly.

  • Developed a Java-based toolset for automated validation and analysis of systems-of-systems models, reducing manual review effort and improving conformance checks.
  • Contributed to a proprietary physics-based simulation environment used to evaluate complex system performance in realistic scenarios.
  • Presented technical tools and collaborated closely with stakeholders to align engineering work with evolving needs.

Projects

A few things I have enjoyed building.

Current project

Network Graph Dashboard

In progress

A dashboard for exploring network graph visuals alongside the metrics that help make those relationships easier to interpret.

  • Building views that pair graph structure with supporting quantitative context.
  • Focusing on making dense network data easier to inspect and reason about.
  • Designing the interface around exploration instead of static reporting.

Team design project

D.A.R.T.S.

Sept 2022 - May 2023

An automated dart scoring system designed to identify thrown dart scores with high precision, paired with an intuitive iPhone app.

  • Built in a five-person team over a year-long design cycle.
  • Implemented the scoring system and supporting SwiftUI mobile experience.
  • Reached 96% scoring accuracy with 2 mm precision.
View project on GitHub