Florian Raudies
Staff Software Engineer at Google
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolFlorian Raudies is a Senior Staff Software Engineer in Mountain View with a decade of experience building large-scale ML-driven recommendation systems at Google, Meta, and LinkedIn. He currently shapes YouTube Homepage recommendations, previously led ranking work for video recommendations and ad-step functions, and has deep research roots in visual motion and bio-inspired navigation from his PhD and postdoc work. Florian combines production-grade engineering with academic rigor—evident in contributions to tensorflow/io where he improved Avro parsing, memory management, and dataset robustness for a widely used TensorFlow extension. He excels at turning ML research into reliable, high-throughput services that impact millions of users and ad monetization. Colleagues rely on him for pragmatic system design that balances model expressivity with operational scalability.
10 years of coding experience
13 years of employment as a software developer
Master’s Degree, Computer Science, Master’s Degree, Computer Science at Ulm UniversityMaster’s Degree, Electrical and Computer Engineering, 3.9, Master’s Degree, Electrical and Computer Engineering, 3.9 at Boston University
German, English
Github Skills (10)
avro10
tensorflow10
python10
datasets10
data-pipelines9
cprogramming-language9
data-pipeline9
c-language9
streaming8
filesystem7
Programming languages (3)
Github contributions (5)
tensorflow/io
Mar 2019 - Mar 2020
Dataset, streaming, and file system extensions maintained by TensorFlow SIG-IO
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 commits, 1 PR, 9 comments in 1 year
Contributions summary:Florian's contributions primarily involve modifications and enhancements to the `tensorflow/io` repository, specifically targeting the Avro data format integration. They fixed issues related to the `make_avro_record_dataset` and `parse_avro` functions, addressing problems with data types and shape handling. The user also refactored and optimized the Avro parsing process, moving keys and schemas to attributes and adding buffer size controls, and improved memory management to address memory growth issues. They also implemented fixes regarding shape and drop remainder in avro record dataset operation.
datasetstreamingsigextensionsfilesystem
fraudies/tensorflow
Apr 2018 - Jun 2019
Computation using data flow graphs for scalable machine learning
Contributions:5 PRs, 28 pushes, 7 branches in 1 year 2 months
computationscalabledataminingdata-sciencemachine-learning
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