Mingyi Lu
Head Of Product And Operations, Founding Team
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMingyi Lu is a product and operations leader with 8 years of engineering and go-to-market experience, currently heading product and ops on a founding team in Palo Alto. He blends hands-on ML engineering—contributing to open-source projects like SGLang for efficient LLM/VLM serving—with product leadership roles at TikTok and a self-funded LLM-powered consumer startup he built from 0 to 1. His background spans research (Stanford CS, computer vision and 3D generation) and applied autonomy (TuSimple perception), giving him a rare mix of research rigor and production-grade systems thinking. At TikTok he led recommendation and engagement initiatives, while his SGLang contributions focused on memory and runtime optimizations that improve model serving efficiency. Mingyi is comfortable recruiting and scaling early teams and is actively maintaining an open-source serving framework, signaling a commitment to tooling that bridges research and deployment. Based in Silicon Valley with an MS from Stanford and a BS in CS & Math, he excels at translating complex ML systems into user-facing products.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS Computer Science and Mathematics, Bachelor of Science - BS Computer Science and Mathematics at University of Wisconsin-MadisonMaster of Science - MS Electrical Engineering (Software Track), Master of Science - MS Electrical Engineering (Software Track) at Stanford University
Github Skills (6)
transformer10
pytorch10
python10
llm10
inference9
cuda8
Programming languages (2)
Github contributions (5)
sgl-project/sglang
Jul 2024 - Aug 2024
SGLang is a fast serving framework for large language models and vision language models.
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 10 PRs, 1 push in 1 month
Contributions summary:Mingyi contributed to the `sglang` project, which focuses on serving large language models and vision language models. Their work includes fixing issues related to the Llava model, specifically addressing code differences in `gemma2.py`, `model_config.py`, `runtime_endpoint.py`, and `infer_batch.py`. The user also made adjustments to reduce workspace usage and simplify memory state management within the system. These commits indicate an involvement in model optimization and efficiency improvements.
cudadeepseekdeepseek-llmdeepseek-v3inference
wisclmy0611/wisclmy0611.github.io
Jul 2017 - Jan 2018
Contributions:119 commits, 107 pushes, 8 branches in 6 months
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