Andre Masella is a Senior Site Reliability Engineer with 20 years of experience building scalable, easy-to-manage infrastructure, currently driving reliability work at Pinterest from Old Toronto. He combines SRE and IaC expertise with compiler and backend work — contributing to LLVM-backed projects like llvmlite and Numba and authoring a custom compiler (Shesmu) for genomic pipelines. At Google he led an IaC code-health initiative for AdSense and automated service onboarding across 20+ teams, and at the Ontario Institute for Cancer Research he redesigned HPC pipelines to cut development time from weeks to days while coaching junior engineers. He has practical systems-forge experience in Rust and WebAssembly, having built a Future scheduler and enabled Disney’s set-top-box test infrastructure. Calling himself a "future legacy systems architect," he bridges production-first reliability, open-source compiler work, and domain science engineering.
20 years of coding experience
12 years of employment as a software developer
Master of Science - MS, Bioinformatics, Master of Science - MS, Bioinformatics at Wilfrid Laurier University
Bachelor of Applied Science - BASc, Computer Engineering, Bachelor of Applied Science - BASc, Computer Engineering at University of Waterloo
A lightweight LLVM python binding for writing JIT compilers
Role in this project:
Back-end Developer
Contributions:46 reviews, 28 commits, 26 PRs in 6 months
Contributions summary:Andre's primary contribution was to deprecate and remove the `llvmlite.llvmpy` module, refactoring its functionality into the `llvmlite.ir` module. This involved adding deprecation warnings, updating documentation, and ultimately removing the deprecated module and related classes. They also made code formatting changes. Furthermore, the user added new LLVM pass bindings and implemented remarks filtering.
Contributions:85 reviews, 44 commits, 35 PRs in 9 months
Contributions summary:Andre's commits primarily focused on refactoring and improving the Numba project's internals. They removed dependencies on deprecated or internal modules and implemented `overload` annotations to replace older `glue_lowering` approaches. These changes involved significant modifications to the codebase, including the implementation of new NumPy datetime and timedelta support and enhancements to the random number generation implementations. Furthermore, they implemented improvements to testing.
cudapythonparallelnumpynumba
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