Radek Osmulski
Senior Data Scientist, Recommender Systems
Queensland, Australia
Summary
🤩
Rockstar🎓
Top SchoolRadek Osmulski is a Senior Data Scientist specializing in recommender systems with 11 years of experience building production ML and data engineering solutions, currently driving research and product work at NVIDIA from Queensland, Australia. He combines a research-oriented background in AI with hands-on software engineering—contributing to large open-source projects like NVIDIA-Merlin's NVTabular where he improved TFRec integrations and optimized groupby operators for terabyte-scale feature engineering. His career spans startups and enterprise roles, from AI research at Earth Species Project and Curai to earlier full-stack and automation work in Ruby on Rails and operations management, demonstrating an unusual blend of product delivery and infrastructure automation. Colleagues rely on him to translate complex data needs into robust, well-tested systems, and outside work he pursues continuous learning about applying technology creatively to real problems.
11 years of coding experience
14 years of employment as a software developer
High School Diploma, High School Diploma at The American School of WarsawSociology, Sociology at Hamline University
Polish, English
Github Skills (14)
machine-learning10
recommendation-system10
feature-engineering10
preprocessing10
python10
preprocess10
documentation10
tensorflow9
gpu9
fastai9
testing8
numpy8
dask8
functional-programming7
Programming languages (10)
Github contributions (5)
NVIDIA-Merlin/NVTabular
Mar 2022 - Sep 2022
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
Role in this project:
ML Engineer
ML EngineerContributions:25 reviews, 68 commits, 12 PRs in 6 months
Contributions summary:Radek contributed to the `nvtabular` repository, a library for feature engineering in tabular data for deep learning recommender systems. Their contributions included updating and improving test cases related to `tf4rec`, a TensorFlow-based library. They added new features and wrapper classes, like `AddTags`, `AddProperties`, and `TagAs` classes, for metadata management. They also worked on optimizing the `Groupby` operator, including adding casting for aggregations and fixing column name issues.
tsneengineeringtensorflowpreprocessingnvidia
AnswerDotAI/fastcore
Dec 2019 - Apr 2022
Python supercharged for the fastai library
Role in this project:Technical Writer
Technical WriterContributions:6 commits, 7 PRs, 3 comments in 2 years 4 months
Contributions summary:Radek primarily contributed by fixing typos, correcting wording, and adding minor documentation improvements to the project. They modified documentation and code comments, including adding missing characters. Their changes are focused on enhancing clarity and accuracy in the documentation.
dispatchloopspythonfunctional-programmingdocumentation-generator
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