# Lucie Ugarte

> Software Engineer at DeepSig Inc.

Location: Bethesda, Maryland, United States
Profile: https://flows.cv/lucie

I’m a software engineer at DeepSig who uses C++ to develop software that detects and identifies signals using machine learning.  This job is perfect for me because I get to incorporate my favorite parts of my electrical engineering background, specifically signals, coding, and general problem-solving.  Another thing I love about working at a small company like DeepSig is that I get to wear many hats.  During my time working here, I’ve had the opportunity to develop my skills in the languages C++, Python, Java, and Typescript, and work with various Nvidia Jetson embedded systems as well as USRP radios.  I’ve also worked on many stages of the development process, including designing and implementing new features, testing, and tracking down bugs to decrease technical debt.  Finally, I’m happy I get to work for a company whose product concretely improves a necessary technology.

Before DeepSig, I studied electrical engineering at the University of Maryland and worked in the Space Systems Lab. For one project, I developed code that sent telemetry data from a robotic system to a control station using ROS (Robot Operating System) and C.

In my free time, I love going to concerts, playing the trumpet, and making comics!

## Work Experience
### Software Engineer @ DeepSig Inc.
Jan 2020 – Present | Arlington, Virginia, United States
- Trained customers to use DeepSig software to train a neural network to detect and identify signals of interest, eventually developing model used in customer product
- Added automatic recording feature, previously only accessible via CLI, to GUI by working with with Sr. developer to refactor handling of recording parameters so they could be altered during runtime, creating getter and setter API calls for those parameters, and adding frontend features to access API calls
- Ported sections of existing backend C++ code from CPU to GPU using CUDA libraries to parallelize code, improving speed of software on devices with multiple GPUs
- Improved default model accuracy by adding “cut out” data augmentation to training pipeline of neural network, by setting random continuous groups of data samples to 0
- Reduced technical debt by identifying underlying issues, implementing solutions, and creating unit tests
- Performed thorough code reviews by checking functionality, readability and style on merge requests
- Completed QA tests each release cycle on Nvidia Jetson Xavier developer kits

### Undergraduate Research Assistant @ Space Systems Labratory
Jan 2018 – Jan 2020 | College Park, Maryland


## Education
### Bachelor's degree in Electrical Engineering
University of Maryland


## Contact & Social
- LinkedIn: https://linkedin.com/in/lugarte

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Source: https://flows.cv/lucie
JSON Resume: https://flows.cv/lucie/resume.json
Last updated: 2026-04-05