Alex Dodge
Backend Software Engineer at Canold
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolAlex Dodge is a backend software engineer with 15 years of experience building scalable, production-ready systems and ML-driven NLP pipelines. Based in San Diego, he blends hands-on backend work—ETL, Kafka, Redis, MySQL, and Kubernetes on AWS—with research-grade natural language processing and machine learning developed in both industry and academia. At NTENT he helped re-architect an NLP query pipeline into microservices and created low-latency C++/Python components and entity-linking models backed by knowledge graphs; more recently he focuses on reliability, CI/CD, and observability in production. An active contributor to hyperopt-sklearn, he has improved hyperparameter optimization for scikit-learn, added new preprocessors and classifiers, and fixed tricky sparse-input and cross-validation bugs. His academic background in cognitive science and computational linguistics informs a pragmatic, data-driven approach to language systems that balances research insight with engineering discipline.
15 years of coding experience
11 years of employment as a software developer
University of California San DiegoM.A., Computational Linguistics, M.A., Computational Linguistics at San Diego State University
English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
scikit10
hyperparameter-optimization10
machine-learning10
python10
scikit-learn10
linear-models9
testing8
unit-testing7
Programming languages (3)
Github contributions (5)
hyperopt/hyperopt-sklearn
Sep 2018 - Jan 2019
Hyper-parameter optimization for sklearn
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 10 PRs, 12 comments in 3 months
Contributions summary:Alex contributed to the `hyperopt-sklearn` project by implementing and improving hyperparameter optimization functionality for scikit-learn models. They addressed bugs related to sparse input and cross-validation. They added features like the `continuous_loss_fn` option for custom loss functions and integrated Lasso and ElasticNet classifiers. Furthermore, the user expanded the project's capabilities by incorporating GaussianRandomProjection and SparseRandomProjection preprocessors.
sklearnoptimizationhyper-parameter-optimizationmachine-learningparameter
adodge/chaiNNer
Jan 2023 - Apr 2023
A node-based image processing and AI upscaling GUI that makes it easy to chain together complex processing tasks.
Contributions:2 PRs, 150 pushes, 22 branches in 2 months
pythoncomputer-visionguiupscalingchain
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