Tristan Penman
Principal Engineer at Vivi
Melbourne, Victoria, Australia
Summary
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Rockstar🎓
Top SchoolTristan Penman is a Principal Engineer based in Melbourne with 13 years of experience building and leading engineering teams across startups and large tech companies. Currently driving architecture and delivery at Vivi after progressing from Senior Engineer to Engineering Manager, he blends hands-on backend development with people leadership and technical strategy. His background includes a stint at Amazon where he contributed to the DSSTNE deep learning engine—work that highlights his comfort with performance-sensitive C++ code, build automation and test-driven improvements. Tristan also co-founded and advised technical efforts in health tech, bringing product empathy and regulatory awareness to engineering decisions. A self-described "hacker at heart" with a Computer Science degree from RMIT, he favours pragmatic refactoring and automation to turn complex systems into reliable, maintainable platforms.
13 years of coding experience
11 years of employment as a software developer
Bachelor of Computer Science, Security, 4.0, Bachelor of Computer Science, Security, 4.0 at RMIT University
Stackoverflow
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Github Skills (6)
c-language10
netcdf10
cprogramming-language10
cicd9
build-automation9
unit-testing9
Programming languages (15)
Github contributions (5)
amazon-archives/amazon-dsstne
Aug 2016 - Dec 2016
Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models
Role in this project:
Backend Developer & Automation Engineer
Backend Developer & Automation EngineerContributions:57 commits, 43 PRs, 25 pushes in 4 months
Contributions summary:Tristan primarily contributed to the project by refactoring and improving the codebase. This includes streamlining logging within a utility, updating headers for C++ standard compliance, and refactoring functions for stream-based operations. The user also implemented a basic Travis CI build, demonstrating a focus on build automation. Finally, the user added unit tests for index loading, enhancing the robustness of the project.
scalableml-modelspythondeep-learningamazon
tristanpenman/mini-pgm
Jun 2021 - Dec 2023
Probabilistic Graphical Model library for Ruby ⚠️ early stage WIP ⚠️
Contributions:22 PRs, 41 pushes, 22 branches in 2 years 6 months
wiprubymachine-learningpredictionsgraphical
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