Tommy Li
Staff Software Engineer at LinkedIn
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTommy Li is a Staff Software Engineer in San Jose with 11 years of experience building cloud-native AI and distributed systems, currently applying GNN, TensorFlow, and PyTorch for scalable training at LinkedIn. He blends serverless and container-first design with practical MLOps, having contributed to high-impact open-source projects like Kubeflow Pipelines, KServe, and IBM’s Fabric for Deep Learning. At IBM he led automation for Watsonx and ML workflows, enabling data scientists to move models from prototype to production faster. Tommy’s work often bridges DevOps and model-serving concerns—adding S3/GCS storage support, autoscaling, and Knative integration—so infrastructure decisions directly improve ML reliability. He’s known for pragmatic engineering that favors interoperability and developer experience, and for surfacing production-focused improvements that aren’t always visible in product demos.
11 years of coding experience
9 years of employment as a software developer
University of California Santa Cruz
Japanese, Chinese, English, Chinese
Github Skills (39)
kubernetes10
pytorch10
docker10
adversarial-attacks10
python10
ibm-cloud10
kustomize10
kubeflow10
machine-learning10
bash10
cloud-infrastructure10
kubectl10
jmix10
dockers10
cicd10
Programming languages (17)
Github contributions (5)
IBM/Scalable-WordPress-deployment-on-Kubernetes
Mar 2017 - Apr 2018
This code showcases the full power of Kubernetes clusters and shows how can we deploy the world's most popular website framework on top of world's most popular container orchestration platform.
Role in this project:
DevOps Engineer
DevOps EngineerContributions:122 commits, 16 PRs, 134 pushes in 1 year 1 month
Contributions summary:Tommy primarily focused on automating the deployment and configuration of a WordPress instance on Kubernetes within an IBM Cloud environment. Their contributions involved writing shell scripts to install necessary CLI tools, manage Kubernetes clusters, and configure the CI/CD pipeline using tools such as Bluemix (now IBM Cloud), kubectl, and Travis CI. They also implemented health checks and automation for cluster creation and worker management. Furthermore, the user was responsible for adjusting deployment scripts and configurations, integrating a toolchain button and deploying the wordpress website.
containersibm-developer-technology-cloudmysqlorchestrationshowcases
IBM/FfDL
Feb 2018 - Sep 2022
Fabric for Deep Learning (FfDL, pronounced fiddle) is a Deep Learning Platform offering TensorFlow, Caffe, PyTorch etc. as a Service on Kubernetes
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:1 release, 1 review, 67 commits in 4 years 8 months
Contributions summary:Tommy contributed to the development and maintenance of the FfDL platform. Their work included adding support for different virtual machine types and related functionalities, as well as integrating new frameworks like Caffe2 and PyTorch with GPU support. Furthermore, the user updated UI dependencies and integrated font-awesome, and added a converting script for FfDL to WML and vice versa. They also updated example code, configuration files and deployment scripts.
pythoncaffe2modelstoragetensorflow
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