Brandon Burroughs
Machine Learning Engineer at Meta
Menlo Park, California, United States
Summary
👤
Senior🎓
Top SchoolBrandon Burroughs is a Machine Learning Engineer in Menlo Park with 11 years of experience building production-scale LLM and NLP systems, currently delivering PyTorch GPU retrieval and ranking models for Meta’s Feed and discovery surfaces. He combines deep applied NLP expertise—from tokenization and topic modeling to regulatory monitoring—with a strong foundation in statistics (MS, UNC) and proven product delivery at scale. At Capital One he led innersourced NLP libraries and deployed automated quality/regulatory monitoring across 100M+ calls, while earlier roles spanned fraud pipelines on Spark, full-stack prototyping, and teaching data science. Brandon is hands-on in ML engineering and software practices, mentoring teams and shipping robust, auditable systems in AWS and big-data environments. His open-source contributions to practical data science courseware reflect a focus on clear, usable NLP tooling and education. Colleagues rely on him to translate messy language data into reliable, production-ready models that meet both technical and compliance constraints.
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-analysis10
data-visualizations10
nltk10
pandas10
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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