Hi there! I’m Brian Lim, a Software Developer on Amazon’s Project Kuiper, working on the Terrestrial Applications Team. I graduated with my M.S. in Computer Science at UC Santa Barbara in 2021. Most of my expertise primarily focuses on Machine Learning Engineering, for which I have experience creating predictive models, and I also have a deep understanding of Deep Learning (pun intended). I also have plentiful experience with Backend Software Engineering.

I served as the President of Data Science UCSB from 2019-2021, a club dedicated to teaching students about Data Science and providing the resources for creating projects within the realm. If you would like to help students build a project or come do a talk about your company and how they use Data Science in the industry, please email them, and they would be happy to talk about any aspect of our organization and potential events. I can also forward any correspondence to our alumni network, and if you are interested in contacting this club’s alumni, please send me an email.

I was the TA for UCSB’s Computer Science Capstone in 2020-2021, for which I managed 10 industry-mentored projects created by students of the class. I helped students navigate through all aspects of software development, especially building software for users and creating requirements.

I have interned in the past at Workday and Muncaster Consulting.

At Workday, I built a project taking metric logs from Workday’s data sources and created a data engineering pipeline to parse those logs and store them in a Hadoop Distributed File System (HDFS) as well as a MySQL database.

At Muncaster Consulting, I worked with FLIR’s image processing and object detection SDK. I built the entire functional and performance testing suite that is still used to make sure their SDK is working deterministically and monitors changes to models’ speed and accuracy. At my most recent employment with Muncaster Consulting, I worked on their internal tool called VisData, which aims to make annotating images for bounding box classification much more streamlined than the current toolset available. I worked on preparing the software for release by streamlining their docker build process and parallelized the code causing bottlenecks during runtime to improve the app’s image serving and loading time by at minimum 4x.

Check out my personal projects or hobbies in the navbar!

My Dijkstra number is 5.
My Erdős number is 4.

Resume – Last Updated: March 27, 2024