# Ashwin Murthy

> Principal Engineer, AI Infrastructure, Google Cloud

Location: San Francisco Bay Area, United States
Profile: https://flows.cv/ashwinmurthy

Artificial Intelligence Infrastructure / Large language models on GCP

Overall Technical lead of the A3 High and A3 Mega VM families on GCP
https://cloud.google.com/blog/products/compute/introducing-a3-supercomputers-with-nvidia-h100-gpus

https://cloud.google.com/blog/products/compute/whats-new-with-google-clouds-ai-hypercomputer-architecture

Previously, I lead the effort to build ultra low latency / high throughput networking technology that is at the heart of enabling large language model training and other high performance computing workloads on Google Cloud.

Previously,
Worked on Google's open source large scale machine learning infrastructure - TensorFlow, as part of the renowned Google Brain team.

Previously:
Distributed databases, Large scale cloud infrastructure, SDN, Storage, Cluster Mgmt

http://conferences.sigcomm.org/sigcomm/2013/papers/sigcomm/p207.pdf

https://github.com/Azure/rtable

## Work Experience
### Principal Engineer @ Google
Jan 2017 – Present | Sunnyvale, California, United States
GPU and TPU supercomputers

https://cloud.google.com/blog/products/compute/introducing-a3-supercomputers-with-nvidia-h100-gpus

Previously, Technical Lead Manager in Google Brain, working on the next generation TensorFlow infrastructure

### Software Engineer @ Uber
Jan 2016 – Jan 2017 | San Francisco Bay Area
- Building Uber's globally distributed transactional datastore. This was Uber's home-grown Spanner like db using open source technology like RAFT consensus and RocksDb

- Big data and storage infrastructure for  self driving cars
Self driving cars rely on large scale ML training based on log data, which tend to be an extremely large number of very small sized files. Traditional storage systems like PG SQL or on-prem tech like parallel file systems have notoriously problematic scaling and perf challenges. I helped build a cloud-native parallel file systems that was built on commodity object storage targeted for this use case. This kind of tech has since then become a staple in the cloud provider itself (see AWS EFS offering)

### Principal Software Engineer/Tech Lead Manager @ Microsoft
Jan 2009 – Jan 2016
Microsoft Azure infrastructure - SDN and NFV for Windows Azure. Worked on various distributed systems infrastructure projects. I was one of the original founders and technical lead for SLB - Azure's distributed layer 4 software load balancer as a Service (SIGCOMM paper https://conferences.sigcomm.org/sigcomm/2013/papers/sigcomm/p207.pdf)

I also worked on several incubation efforts like Azure storage RTable

### Senior Software Development Engineer Lead @ Microsoft
Jan 2008 – Jan 2009
Distributed Transactions and Coordination protocols, Windows Workflow foundation, Windows Communication foundation

### Software Design Engineer @ Microsoft
Jan 2004 – Jan 2008
Software Developement - .NET Framework (Visual Studio, Workflow, Transactions), Windows Server 2008 (longhorn server) - Microsoft Distributed Transaction Coordinator (a windows NT sevice)


## Education
### MS in 2004, Computer Science
UC Santa Barbara

### BE in Computer Science and Engineering
Bangalore University

### Sri Aurobindo Memorial school


## Contact & Social
- LinkedIn: https://linkedin.com/in/ashwinmurthy

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Source: https://flows.cv/ashwinmurthy
JSON Resume: https://flows.cv/ashwinmurthy/resume.json
Last updated: 2026-04-12