Nicholas Laureano

About Nicholas Laureano

I most recently worked as a Software Engineering Intern at Microsoft, working on multi-cluster orchestration within the Azure Kubernetes Service (AKS) org. This summer marks a major milestone as I head into the final terms of my Bachelor\'s studies in Computer Science at the University of Oregon, with minors in Earth Science and Mathematics.My path to tech has been anything but linear. Before entering software, I worked hands-on in the civil construction industry—both on-site and in-office—supporting grading operations and developing cost estimates for large-scale earthwork projects. This experience began at my family\'s business, Tectonic Companies, and continued with a 2024 internship at MTX Contractors in Las Vegas. There, I combined my geospatial interests and programming skills to build a Python application that retrieved and visualized global elevation data using open-source APIs and libraries like NumPy and Matplotlib.In the past year, I’ve developed a growing passion for machine learning and deep learning, particularly in applications that bridge Earth science and technology. I’ve applied convolutional neural networks (CNNs) to classify seismic waveforms by earthquake magnitude, experimenting with model performance and data representation to support early-warning systems and geophysical research.Thanks to early college credits earned through AP exams and dual enrollment, I entered university as a sophomore and am on track to graduate in Winter 2026, roughly 2.5 years after enrolling.Outside of my academic and professional life, I’ve played classical and Latin guitar for most of my life. Whether it’s code or music, I’m always drawn to systems of structure and expression. I’m passionate about building things that bridge disciplines—and I’m excited for what’s ahead.

Experience

1. Software Engineer

   Runic Solutions

   Sep 2025 — Present · Eugene, OR, US

   Developing a fully offline, on-device LLM system using Kotlin, Jetpack Compose, and Google’s MediaPipe Tasks, enabling users to load and swap Gemma-3–based models without network dependency- Eliminated a main-thread inference bottleneck, reducing UI freeze time by ~15 seconds per prompt- Implemented persistent conversation storage with GSON, reducing local data load times by over 30%.

Education

• University of Oregon

  Bachelor of Science - BS, Computer Science

• Advanced Technologies Academy

  College and Career Ready Diploma, Computer Science