Rick Zhou
Quantitative Developer
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolRick Zhou is a Quantitative Developer with nine years of experience building ML systems, distributed service infrastructure, and compiler-accelerated model deployments. He blends research and production chops from CMU’s Catalyst group with hands-on engineering at LinkedIn, where he helped integrate gRPC, service discovery, and next-gen RPC traffic management using Envoy. His open-source contributions span high-impact projects—rest.li consistent hashing and balancer work at LinkedIn, deep improvements to TVM’s compiler stack, and practical enhancements to MLC-LLM and Web-LLM that enable native and in-browser LLM inference. Now based in Shanghai and working at Minghong Investment while continuing CMU research, he moves fluidly between low-level C++/compiler fixes and large-scale distributed design. Colleagues rely on him for subtle performance optimizations and robustness fixes that often live at system boundaries (e.g., KV cache, retries, and balancer strategies).
9 years of coding experience
4 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Carnegie Mellon University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (34)
algorithm10
tvm10
consistent-hashing10
algorithms10
javascript10
webworker10
language-model10
c-language10
python10
command-line-interface10
machine-learning10
load-balancing10
data-structure10
java10
llm10
Programming languages (10)
Github contributions (5)
linkedin/rest.li
Jul 2019 - Nov 2022
Rest.li is a REST+JSON framework for building robust, scalable service architectures using dynamic discovery and simple asynchronous APIs.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:270 reviews, 52 commits, 91 PRs in 3 years 3 months
Contributions summary:Rick primarily contributed to the implementation of a bounded-load consistent hashing algorithm and a consistent hash ring simulator. They added functionality for the D2 balancer, including monitoring metrics for relative strategy in DegraderLoadBalancerStrategyV3Jmx and fixing retry client bugs. Further commits indicate enhancements to client-side retry logic and configuration, as well as cluster subsetting functionality.
architecturesapiscalableasynchronousrestful
mlc-ai/mlc-llm
Oct 2023 - Oct 2024
Universal LLM Deployment Engine with ML Compilation
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:14 reviews, 53 PRs, 22 pushes in 1 year
Contributions summary:Rick primarily focused on enhancing the C++ CLI for the MLC-LLM project, adding features like the model library path override and improving error messages. Their work involved modifying the `cli_main.cc` file to incorporate the new functionality and updating documentation. Additionally, the user made modifications to the Python chat module, suggesting a contribution to the overall deployment and execution of the LLM.
language-modelllmmachine-learning-compilationtvm
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