Garrett Idler
Software Engineer Applied AI at Canary Technologies
Denver Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolGarrett Idler is a Software Engineer specializing in Applied AI with a decade of experience building production-grade ML and LLM systems across startups and large tech organizations. He has shipped scalable data and ML pipelines at Amazon/AWS, contributed notable improvements and tests to the widely-used AutoGluon project, and now architects LLM-powered legal automation and SaaS features using hybrid retrieval and RAG techniques. Comfortable across the stack—from C++ and CUDA for real-time systems to Python, Django, and Vue for web platforms—he blends research-informed methods with pragmatic engineering and MLOps. Garrett also mentors and teaches practical ML, has driven measurable performance wins (e.g., optimized NLP preprocessing and custom metric implementations), and is adept at turning domain expertise into high-impact, audited AI tools.
10 years of coding experience
10 years of employment as a software developer
Master's Degree Dynamic Systems and Controls, Master's Degree Dynamic Systems and Controls at The University of Texas at AustinBachelor of Science (B.S.) Mechanical Engineering, Bachelor of Science (B.S.) Mechanical Engineering at Colorado State UniversityLos Alamos High School
Github Skills (13)
machine-learning10
automated-machine-learning10
pytest10
python10
testing10
ensemble-learning9
tabular9
datatable9
datatables9
forecast8
pandas8
forecasting8
documentation8
Programming languages (4)
Github contributions (5)
autogluon/autogluon
Jul 2022 - Jan 2023
Fast and Accurate ML in 3 Lines of Code
Role in this project:
ML Engineer & QA Engineer/Test Automation Engineer
ML Engineer & QA Engineer/Test Automation EngineerContributions:127 reviews, 43 commits, 60 PRs in 6 months
Contributions summary:Garrett contributed significantly to the testing and implementation of the quantile/pinball loss method within the AutoGluon framework, specifically focusing on unit tests for regression metrics. They also addressed code quality by removing dead code and unused imports and updating repository references. Moreover, the user enhanced the documentation by adding notebook platforms to the AutoGluon landing page and fixing broken tutorial links. The user also added features with pandas calls and updated the tutorials.
forecastingimage-textmlppythonmeta-learning
gidler/autogluon
Jun 2022 - Jun 2023
AutoGluon: AutoML for Image, Text, and Tabular Data
Contributions:52 pushes, 24 branches in 1 year
pytorchimage-textpythondata-sciencedeep-learning
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