Chen Jing
Software Engineer II at Booking.com
Pudong, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolChen Jing is a software engineer with 10 years’ experience specializing in infrastructure, cloud-native systems, and infra-level improvements. Currently at Booking.com after impactful roles at VMware and Improbable, he has deep expertise in Go, Python, Kubernetes, Helm, vSphere/vSAN, and distributed runtime work such as Ray. He has contributed notable open-source improvements to the high-profile Ray project—optimizing vSphere provider, adding frozen VM/OVF deployment support and GPU node handling—demonstrating a focus on reliable, production-ready cluster launchers. Chen pairs systems-level engineering with R&I instincts, having worked on federated learning, cloud-native security tooling, observability, and even patent and technical evangelism. Fluent in cross-cultural collaboration from Shanghai, he brings a pragmatic, self-managed approach to complex operational problems and enjoys turning messy infra edge cases into robust, automatable solutions.
10 years of coding experience
5 years of employment as a software developer
Master Computer Science, Master Computer Science at Tongji UniversitySummer Program Kinesiology , Summer Program Kinesiology at The University of British Columbia
Github Skills (10)
autoscaling10
vsphere10
ray10
deploying10
vmware10
python10
devops10
distribute9
cloud-infrastructure9
infrastructure9
Programming languages (6)
Github contributions (5)
ray-project/ray
Jul 2023 - Jan 2024
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Backend & DevOps Engineer
Backend & DevOps EngineerContributions:42 reviews, 13 PRs, 66 comments in 5 months
Contributions summary:Chen's contributions primarily involve optimizing the vSphere provider for the Ray project. They've focused on improving logging, removing unnecessary code, and fixing connection timeout issues. The user has also added support for deploying Ray nodes from frozen VMs and OVF templates, enhancing the cluster launcher's capabilities. Furthermore, the user contributed to supporting GPU Ray nodes on vSphere and refactored the code base, indicating a strong emphasis on infrastructure and system-level improvements for Ray deployments within a vSphere environment.
pythonconsistsruntimetensorflowserving
JingChen23/cloud-native-security-inspector
Nov 2022 - Jul 2023
This project leverages Harbor to scans and assesses workloads in Kubernetes at runtime. It generates assessment reports in different aspects for the overall security posture of the Kubernetes cluster, and then applies protection rules to workloads to avoid further risks.
Contributions:96 pushes, 82 branches in 8 months
runtimeavoidreportsaspectsworkloads
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