Jakub Bachurski
Software Engineering Intern at Jane Street
Cambridge, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJakub Bachurski is a Cambridge computer science student and software engineer with nine years of practical experience building ML-focused tooling and production systems. He has driven the design and implementation of a Pythonic ONNX framework (Spox) used in production at QuantCo and contributed type-and-shape inference improvements to the widely used ONNX project, including operator support and string-casting fixes. At Jane Street and QuantCo he’s applied functional programming principles to create concise, side-effect-minimizing code for complex computational graphs. A competitive programmer and problem solver, he combines algorithmic rigor with pragmatic engineering—evident in a proof-of-concept that demonstrated ONNX’s Turing-completeness. Based in Cambridge, he’s also organized advanced CS workshops, showing a commitment to teaching and community engagement.
9 years of coding experience
1 year of employment as a software developer
Krajowy Fundusz na rzecz DzieciHigh School Diploma, Matematyka i informatyka, High School Diploma, Matematyka i informatyka at XIV Liceum Ogólnokształcące im. Stanisława Staszica w WarszawieBachelor of Arts - BA, Computer Science, First year - Class I, Rank 9/115, Bachelor of Arts - BA, Computer Science, First year - Class I, Rank 9/115 at University of Cambridge
English, German, Polish
Github Skills (11)
machine-learning10
inference10
deep-learning10
onnx10
shapes10
python10
tensorflow9
pytorch9
data-structure7
data-structures7
algorithms7
Programming languages (5)
Github contributions (5)
onnx/onnx
Aug 2022 - Oct 2022
Open standard for machine learning interoperability
Role in this project:
ML Engineer
ML EngineerContributions:17 reviews, 2 commits, 11 PRs in 2 months
Contributions summary:Jakub primarily contributed to the implementation of type and shape inference capabilities within the ONNX framework. They added inference for several operators related to machine learning, including CategoryMapper, TreeEnsembleRegressor, TreeEnsembleClassifier, and Compress, enhancing the framework's ability to handle various ML model types. Their work involved modifying existing code and adding new test cases to validate the inference functions. Furthermore, the user fixed issues related to string casting in the reference implementation.
pytorchmxnetdeep-learninginteroperabilitymachine-learning
jbachurski/taucheck
Nov 2019 - Sep 2022
Contributions:12 commits, 10 pushes, 1 branch in 2 years 10 months
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