Madhu S
ML Software Engineer at Google DeepMind
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolMadhu S is an ML software engineer with 18 years of experience building production-grade infrastructure, distributed systems, and ML tooling in the San Francisco Bay Area. Currently working on Gemini at Google DeepMind after contributing enterprise security and notebook runtime OS features to Vertex AI, Madhu combines low-level systems insight (OS, networks, compilers) with applied ML infra and natural language understanding. Their open-source work includes notable contributions to Kubernetes (API, DNS, cluster bring-up) and Apache AsterixDB, demonstrating a pattern of improving reliability, federation, and deployment ergonomics at scale. A proven problem-solver who bridges backend engineering and DevOps, Madhu also founded a nonprofit that delivered software to 50+ charities—reflecting a knack for shipping pragmatic solutions and mobilizing engineering for social impact.
18 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS Computer Science and Business Administration, Bachelor of Science - BS Computer Science and Business Administration at University of Southern CaliforniaInternational Baccalaureate Diploma, International Baccalaureate Diploma at Trabuco Hills High School
English, Malayalam, Spanish, Hindi
Github Skills (33)
kubernetes10
aster10
automated-deployment10
apidoc10
api-design10
asterixdb10
testing10
terraform10
java10
javas10
terraformer10
go10
api10
dns10
federation10
Programming languages (9)
Github contributions (5)
kubernetes/api
Oct 2015 - Jan 2017
The canonical location of the Kubernetes API definition.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:26 commits in 1 year 3 months
Contributions summary:Madhu contributed to the Kubernetes API definition, focusing on enhancing node condition reporting and adding new types related to DaemonSet updates. Their work involved modifying Go code files to introduce new node conditions for disk space issues and adding structures for DaemonSet update strategies. The user also implemented unit tests to validate these changes, demonstrating a focus on API design and correctness.
kubernetesk8s-staging
kubernetes-retired/kubernetes-anywhere
Jun 2016 - Aug 2017
[EOL] {concise,reliable,cross-platform} turnup of Kubernetes clusters
Role in this project:DevOps Engineer
DevOps EngineerContributions:11 commits, 10 PRs, 4 pushes in 1 year 1 month
Contributions summary:Madhu's contributions focused on the configuration and deployment of Kubernetes clusters within the "kubernetes-anywhere" repository. They implemented changes to output master IP addresses, improved multi-cluster support by prefixing cluster names in TLS provider names, and configured kubeconfigs. They also made changes to include necessary mounting for the kubelet container to detect persistent disks and updated the kube-proxy to use hyperkube.
kubernetes-cluster
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