Fabian Höring
Senior Staff Software Engineer at Criteo
Greater Paris Metropolitan Region France
Summary
👤
Senior🎓
Top SchoolFabian Höring is a Senior Staff Software Engineer with over a decade of experience building scalable backend systems and ML infrastructure at Criteo, now based in the Greater Paris area. He blends production-grade engineering with privacy-focused research, contributing to W3C groups on private advertising and pioneering experiments in private model training using DP-SGD and trusted execution environments. Fabian is an active open-source contributor—his work on s3fs and MLflow improved S3 compatibility and ML lifecycle backend reliability, and he co-published a private ad dataset now cited in preprint research. A frequent speaker on distributed systems and reproducibility, he combines deep systems knowledge with practical MLOps experience to move privacy-preserving advertising from prototype toward deployable solutions.
10 years of coding experience
17 years of employment as a software developer
Diplôme d'ingénieur, Diplôme d'ingénieur at CentraleSupélecDiplom Informatiker Computer Science, Diplom Informatiker Computer Science at Technische Universität Dresden
German, French, English, Spanish
Github Skills (16)
aws-s310
filesystem10
mlflow10
fsspec10
mlops10
amazon-s310
pyarrow10
back-end-development10
python10
pytest10
s3-bucket10
testing10
cloud-storage9
hdfs9
ml8
Programming languages (9)
Github contributions (5)
fsspec/s3fs
Nov 2019 - Jun 2020
S3 Filesystem
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 2 PRs, 7 comments in 7 months
Contributions summary:Fabian primarily focused on enhancing the `s3fs` library, specifically by adding support for additional S3 filesystem schemes, such as `s3a://` and `s3n://`, to improve compatibility with tools like PySpark. They refactored the code to utilize internal methods for path normalization and protocol stripping. Furthermore, the user implemented the `makedirs` functionality and added corresponding tests. These contributions aimed to improve the usability and robustness of the library's S3 filesystem interaction.
fsfile-storagefilesystemaws-s3aws
mlflow/mlflow
Aug 2019 - Sep 2020
Open source platform for the machine learning lifecycle
Role in this project:Back-end Developer / MLOps Engineer
Back-end Developer / MLOps EngineerContributions:11 commits, 13 PRs, 31 comments in 1 year 1 month
Contributions summary:Fabian primarily contributed to the backend of the MLflow project by modifying the fluent API and tracking components. Their work included adding functionality to retrieve experiments and runs via the client API. They also addressed compatibility issues with pyarrow and HDFS, introducing support for federation viewfs and updating project dependencies. The commits demonstrate an understanding of ML lifecycle management, backend infrastructure, and artifact storage.
pythonlifecyclemlmachine-learningincremental-learning
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