Benjamin Minixhofer
Research Intern at Ai2
Cambridge, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolBenjamin Minixhofer is a PhD student in Computation, Cognition and Language at the University of Cambridge and a research-focused software engineer with a decade of experience bridging ML research and production inference. He has interned at leading AI labs including Ai2, Google DeepMind, Cohere and Huawei, contributing to model engineering, evaluation and deployment across TensorFlow, ONNX and PyTorch toolchains. On GitHub he improved tract’s model inference pipeline to run diverse exports (masked language models, XGBoost/LightGBM) reliably, demonstrating practical cross-framework interoperability skills. Benjamin’s background spans academic research and industry R&D, from data science at H2O.ai to embedded software at ams AG, giving him a rare mix of systems-level engineering and deep learning expertise. Colleagues describe him as pragmatic and curious—someone who turns research prototypes into testable, runnable artifacts.
10 years of coding experience
3 years of employment as a software developer
Bachelor's degree Artificial Intelligence, Bachelor's degree Artificial Intelligence at Johannes Kepler Universität LinzDoctor of Philosophy - PhD Computation Cognition and Language, Doctor of Philosophy - PhD Computation Cognition and Language at University of CambridgeHigh School Diploma Electronics and Software Engineering, High School Diploma Electronics and Software Engineering at HTL Bulme
Stackoverflow
123reputation
1kreached
0answers
1question
Github Skills (15)
rust-lang10
machine-learning10
rust10
inference10
onnx10
tensorflow9
neural-network9
artificial-intelligence9
pytorch8
model-optimization8
lightgbm7
xgboost7
pythonnet6
oop6
python6
Programming languages (9)
Github contributions (5)
sonos/tract
May 2020 - Sep 2020
Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 10 commits, 4 PRs in 3 months
Contributions summary:Benjamin primarily contributed to the model inference capabilities of the repository. They added functionality to convert models into a runnable format, allowing for fixed inputs and outputs. Furthermore, the user integrated and tested models exported from different frameworks like TensorFlow, ONNX, and PyTorch for various tasks such as masked language modeling and tree-based models like XGBoost and LightGBM. The changes involved modifying example code and adding tests to ensure model execution.
rust-libraryno-nonsenseself-containedrustdeep-learning
segment-any-text/wtpsplit
Jan 2020 - Jul 2022
Toolkit to segment text into sentences or other semantic units in a robust, efficient and adaptable way.
Contributions:35 releases, 1 review, 369 commits in 2 years 6 months
pythontext-segmentationboundary-detectionboundaryjavascript
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