Qianli Ma
Shanghai, Singapore
Summary
🤩
Rockstar🎓
Top SchoolQianli Ma is a machine learning systems engineer with six years of experience building and optimizing large-model infrastructure across industry leaders including ByteDance, HPC-AI Tech, SenseTime and Huawei. He specializes in distributed training and inference for large models—contributing to ColossalAI with practical improvements like reduce-scatter refinements, diffusion-model examples, dataset integrations and inference utilities that make large AI models cheaper and faster. With a strong academic foundation from Zhejiang University and an MSc in Computer Science from NUS, he blends systems-level rigor with applied ML research. Based in Shanghai and Singapore, Qianli moves fluidly between research and production, having progressed rapidly from intern to staff research engineer within ByteDance’s Seed MLSys group. An understated strength is his focus on reproducible, production-ready tooling that smooths the path from experimental models to scalable deployment.
6 years of coding experience
3 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at National University of SingaporeSummer student, communication, Summer student, communication at University of Illinois Urbana-ChampaignBachelor's degree, Electrical science and technology, Shannon elite class(香农卓越班）, Bachelor's degree, Electrical science and technology, Shannon elite class(香农卓越班） at Zhejiang University
Github Skills (12)
data-parallel10
diffusion-models10
pytorch10
machine-learning10
data-parallelism10
deep-learning10
pipelining10
parallelization10
ai10
pipe10
pipeline10
distributed-computing10
Programming languages (6)
Github contributions (5)
hpcaitech/ColossalAI
Sep 2022 - Jan 2023
Making large AI models cheaper, faster and more accessible
Role in this project:
ML Engineer
ML EngineerContributions:54 reviews, 19 commits, 70 PRs in 4 months
Contributions summary:Qianli primarily contributes to the development and improvement of machine learning models within the repository. Their commits include code style polishing in modules related to distributed training techniques such as reduce scatter, and the addition of example implementations using diffusion models. The user updated the version of the project. They also incorporated a new dataset for diffusion models, and updated the lightning version, and also added some code related to the inference process.
heterogeneous-trainingcolossal-aifinetuningdeep-learninginference
Fazziekey/Fazziekey.github.io
Dec 2022 - Mar 2023
Personal Blog
Contributions:108 commits, 9 PRs, 420 pushes in 3 months
jekyll
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