Hao Jin
Software Engineer at Meta
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHao Jin is a Staff Software Engineer in Palo Alto with 11 years of experience building high-performance cloud and ML systems, currently driving GenAI efficiency work on Google Cloud Vertex AI. He brings deep C++ and Python expertise and a strong ML-infrastructure track record from Meta and A9/Amazon, where he shipped large-scale LLM optimizations, model-system co-design, and GPU-serving improvements that materially reduced costs and raised throughput. A former EE student turned CMU-trained CS master, Hao blends DSP, circuits and computer-architecture intuition with practical systems engineering, enabling wins like sequence-length-aware dispatching and FP16 quantization without model regressions. He is an active contributor to Apache MXNet—implementing and hardening operators such as Correlation and LeakyReLU—which underscores his commitment to open-source ML tooling. Colleagues rely on him for bridging research and production: migrating training stacks, designing runtime/inductor integrations, and founding resource-aware tooling that informed modeling choices.
11 years of coding experience
8 years of employment as a software developer
Bachelor of Science (BS) Computer Engineering, Bachelor of Science (BS) Computer Engineering at University of Illinois Urbana-ChampaignMaster of Science (M.S.) Computer Science, Master of Science (M.S.) Computer Science at Carnegie Mellon UniversityBachelor of Engineering (BE) Electrical Electronics and Communications Engineering, Bachelor of Engineering (BE) Electrical Electronics and Communications Engineering at Shanghai Jiao Tong University
English, Chinese, Japanese
Github Skills (15)
tensorrt10
algorithm10
numerical-optimization10
code-optimization10
algorithms10
machine-learning10
tensor10
tensorflow10
optimisation10
operation10
python10
optimization10
linear-algebra9
c-language8
cprogramming-language8
Programming languages (5)
Github contributions (5)
apache/mxnet
Mar 2018 - Apr 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:141 commits, 437 PRs, 223 pushes in 2 years 1 month
Contributions summary:Hao's commits primarily focus on enhancing the functionality of the `mxnet` deep learning library. They implemented and improved the `Correlation` operator, making it compatible with various data types, including the addition of comprehensive unit tests. Additional contributions included adding support for the `LeakyReLU` operator, improving the `L2Normalization` operator, and addressing an issue in `sample_multinomial_op.h`, as well as optimizing other existing operators.
pythonschedulerdataflowmutationdata-science
haojin2/peloton
Dec 2016 - Mar 2019
The Self-Driving Database Management System
Contributions:121 pushes, 9 branches in 2 years 3 months
management-systemdatabase-management-systemself-drivingdatabasedatabase-management
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