Brandon Burroughs
Machine Learning Engineer at Meta
Menlo Park, California, United States
Summary
👤
Senior🎓
Top SchoolBrandon Burroughs is a machine learning engineer with a decade of experience building and deploying data-driven products across fintech, consumer tech, and analytics tooling. Based in Menlo Park, he currently helps Meta's ML Infra team ship PyTorch-based retrieval and ranking models at scale, bridging research to production for social feeds. Previously at Capital One, he led NLP package development and data pipelines, mentoring juniors and translating business needs into robust data solutions. His background spans end-to-end ML—from prototyping with Python, NLP techniques (tokenization, NER, TF-IDF, LDA) to building production-grade data processing and visualization tools, and even teaching Data Science at General Assembly. He is a hands-on contributor to open source educational NLP projects, and has cultivated a track record of turning insights into reusable, user-friendly products. He holds an MS in Statistics from UNC Chapel Hill and dual BS degrees in Mathematics/Statistics and Finance from Louisiana Tech, underscoring a strong quantitative foundation and business acumen.
11 years of coding experience
8 years of employment as a software developer
Bachelor of Science, Mathematics and Statistics, 4.0, Bachelor of Science, Mathematics and Statistics, 4.0 at Louisiana Tech UniversityMaster of Science (M.S.), Statistics, Master of Science (M.S.), Statistics at UNC Chapel Hill
Spanish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (16)
scikit10
data-visualizations10
data-analysis10
pandas10
nltk10
data-visualization10
data-visualisation10
nlp10
exploratory-data-analysis10
python10
natural-language-processing10
data-science10
scikit-learn10
machine-learning9
textblob9
Programming languages (6)
Github contributions (5)
justmarkham/DAT5
Mar 2015 - Jun 2015
General Assembly's Data Science course in Washington, DC
Role in this project:
Data Scientist
Data ScientistContributions:91 commits, 83 pushes in 2 months
Contributions summary:Brandon contributed significantly to the project by adding exploratory data analysis and visualization material using the pandas library. Their work included reading, summarizing, and manipulating data, along with generating plots to understand the data distribution. The user's commits also included the implementation of logical filtering, sorting, and grouping functionalities within the pandas framework. Furthermore, they worked on incorporating basic visualization plots.
washington-dcpythonsciencedata-sciencemachine-learning
justmarkham/DAT4
Feb 2015 - Jul 2015
General Assembly's Data Science course in Washington, DC
Role in this project:Data Scientist
Data ScientistContributions:12 commits, 11 pushes, 2 comments in 4 months
Contributions summary:Brandon primarily contributed to the development and refinement of Natural Language Processing (NLP) code within the repository. Their work focused on implementing core NLP techniques such as tokenization, stemming, lemmatization, part-of-speech tagging, stop word removal, Named Entity Recognition, and TF-IDF. They also integrated and demonstrated the use of TextBlob for simplified text processing and explored Latent Dirichlet Allocation (LDA) for topic modeling. The contributions show a strong focus on practical NLP applications.
washington-dcpythonsciencedata-sciencemachine-learning
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