Matt Rickard
Founder at Standard Input
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMatt Rickard is a founder and seasoned software engineer with 13 years of experience building developer tooling, cloud-native infrastructure, and ML deployment workflows from San Francisco. After contributing to Kubernetes-related projects at Google, he led operational improvements and automation across high-profile open-source efforts like Minikube, Skaffold, Kubeflow and Distroless, focusing on reliability, CI/CD and reproducible builds. He combines backend and DevOps expertise—adding robust configuration, logging, and versioning features—to make complex distributed systems more maintainable and debuggable. As founder of Standard Input he now applies that operational rigor to code generation products, bridging developer experience and production-grade automation. His background mixes enterprise software experience at Blackstone, large-scale open-source collaboration at Google, and an MBA from Stanford GSB, giving him both technical depth and product-level judgment. Colleagues would note his penchant for incremental infrastructure improvements that disproportionately reduce operational toil.
12 years of coding experience
5 years of employment as a software developer
MBA, MBA at Stanford University Graduate School of BusinessBA Mathematics, BA Mathematics at Columbia College, Columbia University
Stackoverflow
2,088reputation
249kreached
16answers
0questions
docker
top-5%
kubernetes
top-5%
Github Skills (44)
kubernetes10
docker10
python10
scripting10
testing10
configuration-management10
kubeflow10
machine-learning10
viper10
dockers10
cicd10
gcp10
debian-packaging10
automation10
script10
Programming languages (13)
Github contributions (5)
kubeflow/fairing
Nov 2018 - Apr 2019
Python SDK for building, training, and deploying ML models
Role in this project:
ML Engineer
ML EngineerContributions:35 commits, 43 PRs, 2 pushes in 5 months
Contributions summary:Matt primarily contributed to the development of the `fairing` Python SDK, focusing on building, training, and deploying ML models within a Kubernetes environment. Their work included integrating an append builder, refactoring the builder, preprocessor, and deployer interfaces, adding a serving deployer for prediction endpoints, and updating examples to reflect these changes. The user also addressed issues related to deployment and configuration, ensuring the project's functionality and usability for machine learning tasks.
deployingml-modelspythondata-sciencemachine-learning
GoogleContainerTools/skaffold
Jan 2018 - Dec 2018
Easy and Repeatable Kubernetes Development
Role in this project:Backend & DevOps Engineer
Backend & DevOps EngineerContributions:7 releases, 619 commits, 253 PRs in 11 months
Contributions summary:Matt contributed to the foundational components of the skaffold project by introducing a version command and setting up the basic repository structure. They also added build and test scripts using shell scripting. The user integrated `dep` as the project's vendoring tool, and implemented support for running tests using `go test`, and included the coverage option to improve test quality. Their contributions also extend to the Kubernetes deployment aspect with the addition of kubectl deployer and initial integration tests.
containershelmrepeatabledockerdeveloper-tools
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