Yuki Iwai
Engineer at CyberAgent, Inc.
Tokyo, Japan
Summary
🤩
Rockstar🎓
Top SchoolYuki Iwai is an engineer and technical lead based in Tokyo with five years of experience building on-prem Kubernetes-based AI/ML platforms. He leads and maintains key open-source projects across Kubeflow and Kubernetes — including kueue, MPI Operator and Katib — where his contributions focus on refactoring, dependency upgrades, cert management, CI/CD reliability, and production-ready controller logic. At CyberAgent and as a WG lead and reviewer for Kubernetes batch APIs, he bridges hands-on DevOps and back-end development with governance and API stewardship. His work often targets long-term maintainability: moving v2 operators to top-level repos, reimplementing tooling in Go, and adopting linters and leader election for robustness. Colleagues rely on him for pragmatic technical decisions that reduce legacy debt while enabling distributed training and job-queueing at scale. Outside the codebase, he’s notable for combining deep platform engineering with active leadership roles in both Kubeflow and Kubernetes working groups.
5 years of coding experience
Master's degree, Computer Science, Master's degree, Computer Science at 近畿大学
Github Skills (24)
kubernetes10
docker10
testing10
kubeflow10
dockers10
cicd10
scheduler10
scheduling10
automation10
go10
sched10
kube-scheduler10
kubernetes-pods10
automations10
devops10
Programming languages (13)
Github contributions (5)
kubeflow/trainer
Oct 2021 - Jan 2023
Distributed ML Training and Fine-Tuning on Kubernetes
Role in this project:
DevOps Engineer
DevOps EngineerContributions:1263 reviews, 19 commits, 126 PRs in 1 year 3 months
Contributions summary:Yuki primarily focused on improving the CI/CD processes for the `kubeflow/trainer` repository, particularly related to Go testing and build workflows. Their contributions included adding tests to the CI, fixing linting issues, and adjusting coverage reports. Furthermore, the user upgraded the Go version and addressed issues within the test environment setup, ensuring the continued functionality and reliability of the project's testing infrastructure.
xgboostkubernetesmachine-learningtrainingai
kubeflow/katib
Sep 2021 - Jan 2023
Automated Machine Learning on Kubernetes
Role in this project:Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:911 reviews, 70 commits, 113 PRs in 1 year 4 months
Contributions summary:Yuki primarily focused on enhancing the Katib platform through significant code modifications and infrastructural improvements. They contributed to the reimplementation of the katib-cert-generator in Go, introducing a new command and improving its functionality. Further efforts involved the adoption of golangci-lint for code quality, leading to multiple code fixes and improvements. The user also worked on supporting leader election and enhancing validation procedures within the system, demonstrating expertise in both backend and operational aspects.
xgboosthyperparametermachine-learninglightgbmautoml
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