Nouamane Tazi
Machine Learning Engineer at Hugging Face
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolNouamane Tazi is a Machine Learning Engineer with six years of experience, currently accelerating LLM training workflows at Hugging Face and authoring content for ScalingBook.ai. He blends research-grade ML expertise from CentraleSupélec and Université Paris-Saclay with hands-on MLOps and backend development, contributing to widely used benchmarks like BEIR and MTEB to improve distributed evaluation and kNN tasks. Nouamane has shipped production NLP systems and CI/CD data pipelines in industry internships, and his work often focuses on making GPU-heavy workloads more efficient and scalable. Comfortable across the ML stack, he pairs deep technical rigor with practical engineering—refactoring evaluation frameworks and enabling multi-GPU distributed runs that materially speed up model evaluation.
6 years of coding experience
3 years of employment as a software developer
Master of Science - MS Artificial Intelligence, Master of Science - MS Artificial Intelligence at CentraleSupélecAdvanced mathematics physics and engineering classes, Advanced mathematics physics and engineering classes at Alkhansaa HighschoolMaster of Science - MS Artificial Intelligence, Master of Science - MS Artificial Intelligence at Université Paris-SaclayAdvanced mathematics physics and engineering classes (MP*), Advanced mathematics physics and engineering classes (MP*) at CPGE Moulay Youssef
Arabic, French, English
Stackoverflow
9reputation
108reached
2answers
1question
Github Skills (24)
pytorch10
benchmark10
sentence-transformers10
python10
machine-learning10
benchmarking10
information-retrieval10
nlp10
scikit9
text-classification9
scikit-learn9
deep-learning9
natural-language-processing8
datasets8
jupyter-notebook6
Programming languages (11)
Github contributions (5)
embeddings-benchmark/mteb
May 2022 - Oct 2022
MTEB: Massive Text Embedding Benchmark
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:67 reviews, 162 commits, 23 PRs in 5 months
Contributions summary:Nouamane implemented new tasks related to kNN classification, particularly for the Amazon MASSIVE dataset, including the creation of new files for the task definition and evaluation, and integrating it with SentenceTransformer models. The commits show substantial effort to add and integrate the kNN Classification models with specific modifications to the evaluation framework. The user's work also involved refactoring code and including the use of PyTorch in kNN classification.
bertsimilarity-searchbenchmarkretrievalbug-reporting
beir-cellar/beir
Jun 2022 - Aug 2022
A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
Role in this project:MLOps Engineer
MLOps EngineerContributions:4 reviews, 37 commits, 4 PRs in 2 months
Contributions summary:Nouamane primarily focused on improving the efficiency and performance of the BEIR retrieval system, a benchmark for information retrieval. They addressed issues related to multi-process encoding by fixing the `chunk_size` parameter and removing redundant chunking. Further, they implemented distributed evaluation for multi-GPU setups, contributing to the system's scalability and efficiency in processing large datasets. These changes facilitated the evaluation of models across diverse IR datasets.
diversebertzero-shot-retrievalbenchmarkretrieval
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