Brian Zier
Principal Software Development Engineer at LivePerson
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolBrian Zier is a Principal Software Development Engineer based in Seattle with 15 years of experience building reliable, production-grade systems at LivePerson and state agencies. He holds a MS in Computer Science (3.99 GPA) from Eastern Washington University and has progressed through engineering and senior roles into principal-level leadership at LivePerson. His background spans full lifecycle delivery including systems integration, Linux server operations, virtualization, and automation scripting across Python, Bash, and PowerShell. Brian combines hands-on implementation with architecture and performance work—evidenced by his GitHub contribution optimizing a TensorFlow-based self-driving MarioKart project where he improved image-processing performance and integrated a gym environment for better model training. He’s comfortable in both enterprise and public-sector contexts, translating complex requirements into maintainable, testable solutions. Colleagues would describe him as a pragmatic engineer who pairs deep technical craftsmanship with a steady track record of shipping impactful software.
15 years of coding experience
12 years of employment as a software developer
Master of Science, Computer Science, 3.99, Master of Science, Computer Science, 3.99 at Eastern Washington University
English
Stackoverflow
445reputation
33kreached
19answers
1question
Github Skills (14)
machine-learning10
tensorflow10
performance-optimization10
python10
computer-vision9
openai-gym8
gymnasium8
csv6
dictionary6
visual-studio6
r6
scikit-learn6
network-analysis6
wxpython5
Programming languages (14)
Github contributions (5)
kevinhughes27/TensorKart
Jan 2017 - May 2018
self-driving MarioKart with TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:35 commits, 10 PRs, 1 push in 1 year 3 months
Contributions summary:Brian primarily focused on optimizing the self-driving MarioKart project, enhancing its performance and user interface responsiveness. Their contributions include improving the performance of image processing functions, specifically `take_screenshot()` and `prepare_image()`, which are critical for the machine learning model. The user also added results data to the performance tests and updated TensorFlow calls for compatibility. Moreover, they refactored the code to incorporate a gym environment, indicating efforts to improve the model's training or evaluation process.
racingself-drivingmachine-learningtensorflowtensorflow2
bzier/gym-mupen64plus
Feb 2017 - Mar 2021
An OpenAI Gym environment wrapper for the Mupen64Plus N64 emulator
Contributions:2 reviews, 176 commits, 40 PRs in 4 years 1 month
gym-libraryemulatornintendo-64n64reinforcement-learning
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