Richard Howell
Software Engineer at Meta
Redmond, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolRichard Howell is a seasoned software engineer with 12+ years building developer tooling and mobile systems, currently at Meta after leading iOS Developer Productivity at Uber where he scaled IDE plugins, repo tooling, and a distributed build system to support hundreds of mobile developers. He blends deep build-system expertise (Bazel, Buck, xcodebuild, CI/CD) with low-level language knowledge across Objective-C, Swift, C/C++, and Python, and has contributed build and compiler fixes to major open-source projects like PyTorch and the Swift compiler. His background in signal processing and acoustics informs a pragmatic, performance-first approach to engineering, from graphics shaders to compiler flags. Based in Redmond, he brings a full-stack mindset to developer experience—optimizing both tooling and runtime—while continuing to improve cross-platform build reliability and debugging fidelity.
11 years of coding experience
10 years of employment as a software developer
BSc, Mathematics, BSc, Mathematics at The University of ManchesterMSc, Audio Acoustics, MSc, Audio Acoustics at The University of SalfordMSc, Digital Signal Processing, MSc, Digital Signal Processing at King's College London
English, German
Github Skills (28)
debugging10
objective-c10
debug10
buck10
caffe10
ios10
react-native10
mobile-development10
ui-components10
compiler-development10
build-system10
performance-optimization10
swift10
compiler-flags10
c-language9
Programming languages (15)
Github contributions (5)
swiftlang/swift
Oct 2020 - Dec 2024
The Swift Programming Language
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 reviews, 22 PRs, 6 pushes in 4 years 2 months
Contributions summary:Richard focused on improving the Swift compiler's debugging information generation. They addressed issues related to module handling, particularly when using `-fmodule-file-home-is-cwd`, ensuring correct file paths and compilation directory settings for debugging symbols. The user fixed an ASAN use-after-scope error in a test file. Furthermore, they made changes to correctly set compilation directories and serialized module interface paths, optimizing the build process.
swift-programmingswift-programming-languageprogramming-languageswift
pytorch/pytorch
May 2022 - Nov 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:Back-end Developer
Back-end DeveloperContributions:9 commits, 9 PRs, 1 comment in 6 months
Contributions summary:Richard primarily focused on build system and compiler flag configurations within the PyTorch framework, specifically related to Caffe2 and its dependencies. Their contributions involved moving flags, adding missing ones, and removing unnecessary ones to optimize build processes. The user also made changes to remove configurations and symlinks in xplat, streamlining the build environment. Additionally, they addressed build warnings by disabling unused argument warnings.
pythongpu-accelerationdeep-learninggpunumpy
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