Karl Hornlund
Engineering Manager at Canva
Adelaide, South Australia
Summary
👤
Senior🎓
Top SchoolKarl Hornlund is an engineering manager and machine learning practitioner with a decade of experience building applied ML solutions for industry and government, now leading teams at Canva from Adelaide. He has hands-on expertise across the ML project lifecycle—from business development and requirements to deployment—backed by a Georgia Tech MSc in Machine Learning and a track record at the Australian Institute for Machine Learning solving real-world problems like remote sensing mineral prediction, ecological monitoring, and underwater acoustics. Karl combines research-grade deep learning (PyTorch) and geospatial analysis skills with software engineering experience in C++, C#, and production tooling gained at Saab and large organisations. He contributes to open-source ML infrastructure and testing—improving PyTorch training templates and writing pytest coverage for EfficientNet implementations—showing a focus on reproducible, well-configured training pipelines. Known for bridging multidisciplinary teams and building practical prototypes that win competitions and funding, he brings both technical depth and product-minded delivery.
10 years of coding experience
9 years of employment as a software developer
Bachelor of Engineering - BE Computer Engineering, Bachelor of Engineering - BE Computer Engineering at University of AdelaideOverseas Exchange Computer Science, Overseas Exchange Computer Science at Uppsala UniversityOverseas Exchange Computer Engineering, Overseas Exchange Computer Engineering at McGill UniversityBachelor of Management, Bachelor of Management at University of South AustraliaMSc. Computer Science Machine Learning, MSc. Computer Science Machine Learning at Georgia Institute of Technology
Github Skills (8)
configuration-management10
pytorch10
logging10
pytest10
python10
yaml10
testing10
pre-trained-model8
Programming languages (4)
Github contributions (5)
victoresque/pytorch-template
Apr 2019 - Apr 2019
PyTorch deep learning projects made easy.
Role in this project:
Backend Developer
Backend DeveloperContributions:7 commits, 1 PR, 4 comments in 3 days
Contributions summary:Karl primarily focused on modifying the project's logging functionality, transitioning to a YAML configuration for increased flexibility. They updated base classes, specifically the `BaseTrainer` and `BaseModel`, to incorporate the logging changes and adjusted the logging level configuration. Furthermore, the user tested training, resume, and test functionalities, and corrected the setting of log verbosity within the `base_trainer`. This indicates a focus on core training infrastructure and configuration.
deep-learningpytorchmachine-learning
lukemelas/EfficientNet-PyTorch
Sep 2019 - Sep 2019
A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:5 commits, 1 PR in 1 day
Contributions summary:Karl primarily focused on testing the `efficientnet-pytorch` model. Their contributions involved writing and updating test cases in `test_model.py` using the `pytest` framework. These tests covered various aspects of the model, including forward pass, dropout behavior, and the ability to modify the model's architecture, specifically focusing on the final layers like the head and pooling.
imagenetpytorchefficientnetv2coming-soonfeature-extraction
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