Tobias Kupek
Engineering Lead at Good Research
Innsbruck, Tyrol, Austria
Summary
🤩
Rockstar🎓
Top SchoolTobias Kupek is an Engineering Lead based in Innsbruck with 11 years of experience building machine learning, security & privacy, and data science systems. He combines hands-on ML engineering—contributing robust metrics like Mean Average Precision to the popular torchmetrics library—with leadership in privacy forensics and software reverse engineering at Good Research. Tobias has shipped production computer vision models for traffic analytics, developed B2B e-commerce platforms, and delivered bespoke data tools for industry clients, demonstrating depth across research and applied engineering. Known for pragmatic, testable code and a passion for high mountains, he pairs technical mentorship with cross-team alignment to turn complex privacy and ML challenges into auditable solutions.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BS, Management Information Systems, 1.6 / A-, Bachelor of Science - BS, Management Information Systems, 1.6 / A- at Hochschule Hof, University of Applied SciencesMaster of Science - MS, Computer Science, 1.3 / A, Master of Science - MS, Computer Science, 1.3 / A at Leopold-Franzens Universität Innsbruck
English, German
Stackoverflow
37reputation
3kreached
2answers
5questions
Github Skills (16)
object-detection10
pytorch10
machine-learning10
deep-learning10
python10
metric10
computer-vision9
data-science9
testing8
cmake6
dialogflow-es6
aop6
ant6
actions-on-google6
dialogflow-fulfillment6
Programming languages (8)
Github contributions (5)
Lightning-AI/torchmetrics
Oct 2021 - Dec 2021
Machine learning metrics for distributed, scalable PyTorch applications.
Role in this project:
ML Engineer
ML EngineerContributions:40 reviews, 9 commits, 6 PRs in 2 months
Contributions summary:Tobias primarily contributed to the implementation and refinement of the Mean Average Precision (MAP) metric within the `torchmetrics` library. Their work involved adding the MAP metric for object detection, fixing issues related to empty predictions and CUDA execution, and creating an example for the metric's usage. They also addressed edge cases, improved the metric's robustness, and ensured proper integration with the PyTorch ecosystem.
pytorchscalablepythonanalysesdata-science
tkupek/cnn-keyleakage
Dec 2019 - May 2020
Contributions:53 commits, 37 pushes, 1 branch in 4 months
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