Henry Jiang
Compiler Engineer at Apple
Vancouver, British Columbia, Canada
Summary
🤩
Rockstar🎓
Top SchoolHenry Jiang is a compiler engineer with five years of experience focused on LLVM optimizations, Clang, and Rust, currently working on compiler technology at Apple. He has contributed upstream to the high-profile llvm/llvm-project—implementing vectorization and code generation improvements like VectorCombine enhancements and PPC inlining fixes—and is an LLVM committer. Prior roles span performance-critical systems and cloud infrastructure: at IBM he maintained PowerPC targets and worked on BOLT and JITLink, and at SS&C he engineered highly available Postgres clusters and Kubernetes deployments that materially improved SLA and reduced costs. Henry combines low-level compiler expertise with practical production ops skills, from loop optimization patches to running multi-AZ databases and k8s clusters. Based in Old Toronto and trained at the University of Waterloo in Honours Math/CS, he brings a blend of rigorous algorithmic thinking and hands-on delivery across compiler toolchains and distributed systems. An attention-to-detail shows in both architecture (PITR and WAL streaming) and subtle compiler semantics (preserving FP fast-math flags).
5 years of coding experience
3 years of employment as a software developer
Honors Mathematics Computer Science, Honors Mathematics Computer Science at University of Waterloo
Github Skills (6)
compiler-optimization10
c-language10
cprogramming-language10
llvm10
vectorization8
flang6
Programming languages (7)
Github contributions (5)
llvm/llvm-project
May 2024 - Apr 2025
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
Role in this project:
Backend Developer
Backend DeveloperContributions:19 reviews, 25 PRs, 6 pushes in 10 months
Contributions summary:Henry primarily contributed to the LLVM compiler infrastructure by implementing and modifying code related to vectorization and code generation. Their work involved enhancing the `VectorCombine` pass, which included preserving floating-point fast math flags for improved optimization, and implementing the `areInlineCompatible` function for the PPC target, ensuring correct inlining behavior. They also worked on loop optimization, specifically recognizing and replacing strlen/wcslen loops. Additionally, they contributed to fixing issues on the PPC architecture by XFAILing specific tests.
compilerstechnologiesclangsubmittoolchain
mustartt/ml-marketplace
Aug 2021 - Oct 2021
Contributions:4 reviews, 209 commits, 40 PRs in 2 months
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