Greg Brener
Research Engineer at XBOW
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolGreg Brener is a research engineer and founder with 11+ years of hands-on experience building high-performance software across FinTech, semiconductors, scientific computing, and public-sector projects for organizations like NASA and the US Army ERDC. He combines an electrical engineering foundation from Purdue with deep full‑stack and machine‑learning tooling expertise—C++, Python, distributed systems, and data engineering—to ship scalable financial products and train LLMs on custom accelerator clusters. Greg has led multi-currency banking integrations at Wise, reduced credit losses and sped accounting cycles at Stripe, and optimized ML training at Habana/Intel, while contributing performance-focused improvements to notable open-source projects such as datashader and intake. Comfortable from low-level RTL and DFT work to cloud-native data pipelines and observability, he has a track record of squeezing latency and cost out of complex systems. Based in Austin, he pairs startup grit—iterating a stealth product pre-PMF—with research-grade rigor and public, well-documented open-source contributions.
11 years of coding experience
14 years of employment as a software developer
Bachelor of Science, Electrical Engineering, Bachelor of Science, Electrical Engineering at Purdue University
Stackoverflow
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sed
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sqlite
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linux
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Github Skills (35)
performance-analytics10
performance-monitor10
sqlite10
python10
pandas10
performance-measurement10
performance-analysis10
dask10
performance-tuning10
data-catalog10
performance-monitoring10
awk9
data-visualizations9
pytest9
numpy9
Programming languages (8)
Github contributions (5)
holoviz/datashader
Mar 2017 - Dec 2017
Quickly and accurately render even the largest data.
Role in this project:
Data Scientist & Performance Engineer
Data Scientist & Performance EngineerContributions:73 commits, 19 PRs, 93 pushes in 8 months
Contributions summary:Greg primarily focused on benchmarking and optimizing the performance of data processing and visualization tasks within the datashader library. They made several updates to the `filetimes.py` script, which is used to measure read/write times for various data formats. The commits also involved addressing performance issues related to categorical data, fine-tuning caching strategies, and updating package dependencies to leverage the latest performance improvements in libraries such as numpy, fastparquet, and python-snappy.
renderrasterizationdata-visualizationsdatashaderholoviz
intake/intake
Jan 2018 - Feb 2018
Intake is a lightweight package for finding, investigating, loading and disseminating data.
Role in this project:Back-end Developer
Back-end DeveloperContributions:7 commits in 3 days
Contributions summary:Greg primarily focused on enhancing the catalog functionality within the intake repository. Their contributions included adding features for template expansion using shell commands and environment variables. They also addressed cross-platform compatibility in unit tests and refined the project's documentation by fixing docstrings and error messages. The user's work involved modifications to core files related to cataloging and testing.
pythondata-sciencedata-accessfindingsinger
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