Jinjing Zhou
Co-Founder at TensorChord
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolJinjing Zhou is a software engineer and co-founder with 11 years of experience building developer tools and ML systems, currently leading TensorChord from Shanghai to deliver productivity-focused products like ModelZ and pgvecto.rs. Formerly a Machine Learning Engineer II at AWS, she blends production-grade ML experience with full‑stack and DevOps skills—evidenced by contributions to envd that added SSH support, runtime mounts, and CI/CD Pypi releases. She has hands‑on research and graph-ML background, implementing a capsule network in DGL to push deep learning on graphs. Comfortable across startups, research, and enterprise settings, she navigates product, infrastructure, and model concerns end-to-end. On GitHub she focuses on building great tools that make data science workflows reproducible and deployable. Colleagues describe her as a pragmatic engineer who turns complex ML infra needs into reliable developer-facing solutions.
11 years of coding experience
4 years of employment as a software developer
New York UniversityBachelor's degree, Bachelor's degree at New York University ShanghaiGlobal Study Program, Global Study Program at New York University - Leonard N. Stern School of Business中学, 高中, 中学, 高中 at High School Affiliated to Fudan University
Github Skills (16)
pytorch10
machine-learning10
docker10
go10
deep-learning10
graph-neural-network10
devops10
dockers10
cicd10
dgl10
build-automation9
python8
buildkite7
developer-tools7
buildkit7
Programming languages (17)
Github contributions (5)
dmlc/dgl
Sep 2018 - Apr 2022
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
ML Engineer
ML EngineerContributions:276 reviews, 214 commits, 519 PRs in 3 years 8 months
Contributions summary:Jinjing implemented a capsule network model with DGL library, including the core layers and routing algorithm. They created new files and examples related to the capsule network, specifically involving the DGL implementation and testing. The contributions suggest work on deep learning for graphs.
pytorchpythondeep-learningmachine-learninggraph-neural-networks
tensorchord/envd
Apr 2022 - Jan 2023
🏕️ Reproducible development environment
Role in this project:Full-stack & DevOps Engineer
Full-stack & DevOps EngineerContributions:4 releases, 158 reviews, 54 commits in 8 months
Contributions summary:Jinjing contributed to the development of reproducible development environments. They implemented support for SSH, including key pair generation, SSH configuration, and integration with a build process. Additionally, they introduced runtime mount directory capabilities and added features for managing environment resources. Furthermore, the user worked on the project's CI/CD pipeline, specifically focusing on the Pypi release pipeline.
envdexperiment-trackingai-mlbuildkitdata-science
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