Raul Silvera
Software Engineer at Tesla
Dublin, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRaul Silvera is a seasoned software engineer with 12+ years focused on performance analysis, optimization, and concurrency, currently building software at Tesla after roles at Waymo and Google. He has deep systems and compiler experience from a long tenure at IBM and a Master’s in Computer Science from McGill, which underpin his work on low-level performance tooling. Raul is an active contributor to the Go ecosystem—having improved pprof and Go’s profiling internals—bringing real-world profiling expertise to large-scale, concurrent systems. He combines academic rigor with production-hardened engineering, often tackling subtle sampling, symbolization, and profiling-edge cases that boost observability. Based in Dublin, California, he excels at turning performance research into maintainable tools and practical optimizations used across industry.
12 years of coding experience
9 years of employment as a software developer
Computer Engineer, Computer Science, Computer Engineer, Computer Science at Universidad Simón BolívarMaster of Science (MSc), Computer Science, Master of Science (MSc), Computer Science at McGill University
Github Skills (17)
testing10
go10
performance-optimization10
profiling10
golang10
pprof10
protobuf8
protobuffer8
architectures8
lang8
programming-language8
architecture8
algorithm7
algorithms7
data-structure7
Programming languages (4)
Github contributions (5)
google/pprof
Jan 2016 - Dec 2019
pprof is a tool for visualization and analysis of profiling data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:159 commits, 137 PRs, 67 pushes in 3 years 11 months
Contributions summary:Raul primarily focused on updating and refactoring the codebase to adhere to Go conventions. Their work involved significant changes to various internal packages within the `pprof` project, including `internal/symbolizer`, `internal/driver`, and `profile`. These changes involved code modifications across multiple files, indicating an effort to improve the overall structure and maintainability of the profiling tool. Additionally, the user removed testing wrappers, replacing them with native Go mocks, which improved the testing infrastructure of the project.
data-profilingprofilingperformance-analysisperformancepprof
golang/go
Sep 2015 - Mar 2019
The Go programming language
Role in this project:Back-end Developer
Back-end DeveloperContributions:2 PRs, 30 comments, 1 issue in 3 years 6 months
Contributions summary:Raul primarily focused on improving the Go programming language's profiling tools, specifically the pprof functionality. Their contributions involved enhancing heap sampling techniques, optimizing the fastlog2 implementation, and correctly tracking locations for goroutine profiles. Furthermore, the user addressed issues related to the cmd/pprof tool, including supporting packed encoding for repeated fields and re-enabling weblist and disasm commands. They also updated comments and vendorized the pprof tool from github.com/google/pprof.
golanggopluscompilerprogramming-languageinterpreter
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