Chang She
CEO Co-Founder at LanceDB (YC W22)
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChang She is a seasoned founder and engineering leader with 14 years building data infrastructure and ML systems, currently CEO and Co‑Founder of LanceDB, a YC-backed startup creating a developer-friendly vector database for multimodal AI. He combines hands-on backend and release engineering—contributing Python, C++, and build automation to high-impact open-source projects like LanceDB, Lance, and pandas—with strategic product leadership from startups to enterprise at Tubi and Cloudera. At Tubi he scaled engineering teams and architected ML stacks that drove experimentation and personalization, and earlier co-founded and exited a BI startup acquired by Cloudera. A dual EECS and political science graduate from MIT, he pairs quantitative finance roots with practical engineering fluency, and has a track record of shipping core features such as versioning and retrieval primitives that make long-term LLM memory and multimodal search practical.
14 years of coding experience
14 years of employment as a software developer
B.S. M.Eng. Poli Sci (B.S.) EECS (B.S. M.Eng.), B.S. M.Eng. Poli Sci (B.S.) EECS (B.S. M.Eng.) at Massachusetts Institute of TechnologyHigh School, High School at Northview
English, Chinese
Stackoverflow
16,800reputation
3.1mreached
100answers
1question
pandas
top-1%
dataframe
top-5%
pivot
top-5%
matplotlib
top-1%
python
top-1%
numpy
top-5%
Github Skills (32)
pyarrow10
python10
matplotlib10
pandas10
data-structure10
numpy10
release-management10
build-automation10
data-structures10
unit-test10
data-analysis10
data-science9
back-end-development9
c-language9
pivot9
Programming languages (9)
Github contributions (5)
lancedb/lancedb
Mar 2023 - Apr 2025
Developer-friendly, embedded retrieval engine for multimodal AI. Search More; Manage Less.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:16 releases, 306 reviews, 249 PRs in 2 years
Contributions summary:Chang implemented the initial Python implementation for a new LanceDB database feature. This involved creating core database functionality within the `lancedb/db.py` and testing modules, focusing on fundamental operations like table creation and data addition. The contributions demonstrate expertise in utilizing the Lance library, integrating various data types, and building a foundational structure for a retrieval engine designed for multimodal AI applications. They were also responsible for the addition of methods for versioning such as `checkout` and `restore`.
approximate-nearest-neighbor-searchimage-searchnearest-neighbor-searchrecommender-systemsearch-engine
lancedb/lance
Jul 2022 - Jan 2023
Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..
Role in this project:Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:37 releases, 861 reviews, 143 commits in 6 months
Contributions summary:Chang contributed to the Python extension for the Lance project. Their work included setting up the project, publishing to PyPI, and fixing release issues. They also worked on the build process, including restoring the custom build script for PyArrow and updating dependencies, demonstrating a focus on build automation and release management. Additionally, the user made changes to the core C++ code, indicating backend development skills.
polarspythonspatialarrowbioinformatics
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