Bastien Vallet
Tech Lead at Criteo
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolBastien Vallet is a Paris-based Tech Lead with 10 years of software engineering experience, currently leading the Dynamo Efficiency team at Criteo after progressing through senior and staff engineering roles. He brings deep systems and infrastructure expertise—especially Python-driven back-end, DCIM and embedded Linux stacks—gained across Criteo, Seagate/LaCie and earlier embedded roles. Bastien pairs hands-on engineering with team leadership, having managed developer teams building middleware and large-scale data center tooling. He’s an active contributor to MLflow, improving code quality and ML-specific modules such as TensorFlow/Keras integrations and the model registry, reflecting a practical interest in the ML lifecycle. Comfortable across C, Python, Django and infrastructure automation, he combines low-level performance tuning experience with pragmatic platform-level design.
10 years of coding experience
18 years of employment as a software developer
Preparatory school for the High Ingeneer School Science Technology Mathematics, Preparatory school for the High Ingeneer School Science Technology Mathematics at Lycée Carnot DijonDepartement TELECOM ENSERG - ENSIMAG Telecom Computer Science, Departement TELECOM ENSERG - ENSIMAG Telecom Computer Science at Grenoble INP - UGABaccalauréat Scientifique, Baccalauréat Scientifique at Lycée Nièpce
French, English, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
keras10
machine-learning10
mlflow10
model-management10
tensorflow10
python10
ai9
Programming languages (12)
Github contributions (5)
mlflow/mlflow
Oct 2019 - Nov 2019
Open source platform for the machine learning lifecycle
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 23 PRs, 10 comments in 25 days
Contributions summary:Bastien primarily focused on cleaning up and refactoring code related to the project's underlying structure, parameter names and code indents, and miscellaneous methods. They also addressed and fixed issues related to linting, specifically within the TensorFlow and Keras modules, demonstrating involvement in ML-specific tasks. Furthermore, the user's contributions extended to the project's model registry and configuration, indicating efforts toward optimizing the overall functionality of the machine learning lifecycle platform.
pythonlifecyclemlmachine-learningincremental-learning
criteo-forks/prophet
Feb 2020 - Apr 2022
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Contributions:5 releases, 9 pushes, 2 branches in 2 years 1 month
forecastingtime-series-analysisforecastslinearnon-linear
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