Alan Bernstein
Software Engineer - XR Audio at Meta
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolAlan Bernstein is a software engineer specializing in XR audio and DSP with 10 years of experience building high-performance systems for real-time applications. Currently at Meta, he combines algorithm development and automated data analysis with a strong background in signal processing from roles at Molecula, Kuma Signals, and Applied Research Labs. He has practical experience optimizing back-end databases for ML use cases—contributing performance improvements and benchmarks to the FeatureBaseDB project—and has strengthened testing and Python 3 compatibility in widely used open-source tooling like matplotlib. Alan’s blend of research-driven signal processing (foveation algorithms and low-SNR synchronization) and production engineering enables him to translate complex algorithms into robust, scalable implementations. Based in Austin, he pairs an MS in Communications from UT Austin with hands-on systems work, including patent-level wireless projects and SBIR proposal experience.
9 years of coding experience
13 years of employment as a software developer
MS, Communications, Networks and Systems, MS, Communications, Networks and Systems at The University of Texas at AustinBS, Electrical Engineering, BS, Electrical Engineering at Purdue University
Github Skills (18)
pytest10
python10
matplotlib10
testing10
databases10
database10
benchmark9
benchmarking9
bitmap9
go9
bitmaps9
data-visualization8
indexof8
data-visualisation8
big-data8
Programming languages (17)
Github contributions (5)
FeatureBaseDB/featurebase
Dec 2016 - Dec 2021
A crazy fast analytical database, built on bitmaps. Perfect for ML applications. Learn more at: http://docs.featurebase.com/. Start a Docker instance: https://hub.docker.com/r/featurebasedb/featurebase
Role in this project:
Back-end Developer
Back-end DeveloperContributions:522 commits, 105 PRs, 76 pushes in 5 years 1 month
Contributions summary:Alan focused on improving the database's functionality and performance. They implemented string representations for durations in JSON output and added comments for improved code readability. Furthermore, they added a new benchmark with efficient random ID permutations related to Zipf distribution. Their work involved modifying core database functionalities and performance optimization.
pythonperfectml-applicationsfiredata-science
matplotlib/matplotlib
Dec 2016 - Jan 2017
matplotlib: plotting with Python
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:5 commits, 3 PRs, 2 comments in 20 days
Contributions summary:Alan primarily focused on adding and improving tests for the matplotlib library. They added a new test case to verify the behavior of deprecated rc warnings, ensuring that the filename is included in the warning message. The user also fixed typos and a Python 3 compatibility issue within the test suite, contributing to the overall robustness and maintainability of the project's testing infrastructure. Their work directly supports the quality assurance process for matplotlib.
pythondata-sciencegtkdata-visualizationplotting
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