Said Bleik
Principal Applied ML Scientist Mgr at Microsoft
United States
Summary
🤩
Rockstar🎓
Top SchoolSaid Bleik is a Principal Applied ML Scientist and manager at Microsoft with 11 years of industry experience building production-grade machine learning solutions. He combines deep academic training in information systems (PhD-level) and computer science with hands-on experience from roles at Nokia and Lebanon’s Ministry of Finance, giving him a strong bridge between research, enterprise constraints, and public-sector systems. At Microsoft he leads applied ML efforts and contributes to high-impact open-source NLP tooling, including practical BERT text-classification pipelines in Microsoft’s nlp-recipes repository. Colleagues know him for turning complex natural language problems into reproducible, deployable workflows and for mentoring teams to operationalize models at scale. Less obvious is his long arc from government software engineering to leading ML teams, which informs a pragmatic, compliance-aware approach to model development and deployment.
11 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD) Information Systems, Doctor of Philosophy (PhD) Information Systems at New Jersey Institute of TechnologyMaster of Science (MS) Computer Science, Master of Science (MS) Computer Science at Lebanese American University
Stackoverflow
66reputation
2kreached
2answers
0questions
Github Skills (5)
transformers10
machine-learning10
python10
bert10
azure6
Programming languages (4)
Github contributions (5)
microsoft/nlp-recipes
Apr 2019 - Sep 2021
Natural Language Processing Best Practices & Examples
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 573 commits, 168 PRs in 2 years 4 months
Contributions summary:Said implemented functionality to download, read, and preprocess a dataset for text classification using BERT. They created a function to preprocess data for the model and incorporated the use of the BERT tokenizer. Furthermore, the user added a complete example demonstrating how to fine-tune a model for text classification using BERT.
natural-language-understandingnluword-embeddingslanguage-processingdialogue-systems
saidbleik/batchai_mm_ad
Apr 2018 - Aug 2018
Contributions:21 commits, 17 pushes, 1 branch in 3 months
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