Emmanuel Turlay
Oakland, California, United States
Summary
🤩
Rockstar🎓
Top SchoolEmmanuel Turlay is a machine learning infrastructure engineer and founder with 15 years of experience building data and ML platforms that scale, from feature and model stores to training clusters and orchestration. After a PhD and postdoc at CERN, he transitioned from web back-end engineering at startups to tech leadership and platform roles at Instacart and Cruise, then founded Airtrain.ai to simplify LLM fine-tuning and evaluation. He now focuses on designing robust, maintainable ML pipelines and infrastructure at Weights & Biases and CoreWeave, favoring clarity, sane architecture, and long-lived systems. An active contributor to ML tooling (notably work on Instacart’s lore library) he bridges researcher and engineering needs to make machine learning approachable and production-ready. Based in Oakland, he combines scientific rigor with pragmatic delivery and a preference for high standards and diverse, respectful teams.
15 years of coding experience
15 years of employment as a software developer
PhD Fundamental physics, PhD Fundamental physics at Paris-Sud University (Paris XI)BSc Math Physics and Computer Science, BSc Math Physics and Computer Science at Université Paris CitéMSc Physics, MSc Physics at University of Glasgow
English, French, German
Github Skills (8)
pandas10
machine-learning10
data-pipelines10
data-pipeline10
python10
aws8
sqlalchemy7
boto7
Programming languages (5)
Github contributions (5)
instacart/lore
Oct 2017 - Jan 2018
Lore makes machine learning approachable for Software Engineers and maintainable for Machine Learning Researchers
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 8 PRs, 6 pushes in 3 months
Contributions summary:Emmanuel primarily contributed to the `lore` library, focused on machine learning. Their work includes modifications to encoders and pipelines, likely in preparation for features such as a OneHot encoder and improving the handling of test sizes. They also refactored code to conserve the order of features within the encoders. Furthermore, there are contributions related to AWS configuration and database connections.
pythondata-sciencemachine-learningresearchersgluon
sematic-ai/sematic
Apr 2022 - Jan 2023
An open-source ML pipeline development platform
Contributions:10 releases, 1122 reviews, 624 commits in 9 months
pythonml-pipelinespipelinedata-scienceml
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