Nick Lambourne
Engineering Manager (AI) at Canva · Sydney, Australia
I'm an Engineering Manager (AI) at Canva in Sydney, where I lead the Evaluation Platform team — building the tooling we use to measure the quality of Canva's generative AI features, work that touches over 265 million users every month.
I studied at the University of Queensland, earning bachelor's degrees in finance, psychology, and computer science, and I'm now working through an MBA at Melbourne Business School. Along the way I tutored object-oriented programming, systems engineering, and computational linguistics, and spent far too much time building up the UQ Computing Society.
This site is an excuse to keep my hands in frontend development. Have a look at the projects I work on, what I'm listening to, or how I'm tracking against my annual reading challenge.
Currently reading
Rules of Engagement
Ward Larsen
On repeat this week
Slow Bloom
The Home Team
Latest post
Getting That Grad/Intern Role
Jun 24, 2021
Built with Next.js + Mantine, bundled by Bazel, deployed on Heroku, and backed by MongoDB. It's one of the projects I tinker with.
Engineering Manager (AI)
Dec 2024 - Present
Founded and lead the Evaluation Platform team — six engineers building the internal tooling Canva uses to measure the quality of its generative AI features
Led delivery of a head-to-head "arena" service that pits generative AI models against each other across mediums (text, image, video, audio, 3D) and criteria, with first-class support for multi-turn agentic workflows
Shipped tooling that brings the ergonomics of pytest, jest, and JUnit to non-deterministic generative AI features, later extended to continuous evaluation on live production data
Host of Canva's recurring internal AI Forum (200–400 attendees) and interviewer for 70+ ML engineering candidates
Technologies & Skills:
Master of Business Administration
2026 - 2028
MBA at Melbourne Business School (in progress)
Senior ML Engineer
Feb 2021 - Dec 2024
Progressed from Associate Data Engineer to ML Engineer to Senior ML Engineer on the ML Platform team
Built and operated the ML Platform used by 100+ engineers and researchers to train and serve 150+ production models, supporting features reaching over 265 million users monthly
Cut ML platform fixed costs by 85% (>$500k saved annually) by rebuilding the cross-region Kubernetes training clusters around a scale-to-zero Karpenter architecture
Shipped a Java library that streams live inference inputs and outputs from production ML services through AWS Kinesis Firehose to S3 for downstream reinforcement learning
Owned the distroless Nix/Bazel base images that all ML training and serving compute ran on, and designed the onboarding program used by every new ML engineer
Technologies & Skills:
Senior Research Assistant
Jun 2018 - Feb 2024
Architected and developed Elpis, a user-friendly automatic speech recognition (ASR) tool with a React/Flask GUI over multiple back-end engines
Built on the Kaldi ASR library (a complex C++/Bash stack); found traditional HMM/GMM methods outperformed neural approaches on the low-resource Indigenous languages we worked with
As a member of the Centre of Excellence for the Dynamics of Language, ran workshops for language experts at conferences (PULiiMA) and the CoEDL Summer School
Technologies & Skills:
Site Reliability Engineering Intern
Nov 2020 - Feb 2021
I was attached to the Platform SRE team
Worked on full stack projects related to reporting service level objective (SLO) performance to internal teams
Technologies & Skills:
Teaching Assistant
Jul 2018 - Dec 2020
Tutored and prepared materials for various computer science courses
Courses included: Voyages in Language Technologies (COMP3001), Computer Systems Principles and Programming (CSSE2310), and Programming in the Large (CSSE2002)
Technologies & Skills:
Software Engineering Intern
Dec 2019 - Feb 2020
Built a shell-to-Python transpiler framework and reference generator (Python, Flask, React) for Canva's infrastructure team, with an interactive in-browser playground for real-time transpilation
Integrated the AST library from ShellCheck (Haskell) to parse Bash into an intermediate representation suitable for conversion to Python
Technologies & Skills:
Software Engineering Intern
Jan 2019 - Feb 2019
Developed parallelisation strategies for training recurrent neural nets to detect sepsis from vital-sign data
Introduced Python typing, type-checking, and documentation practices as the team's first sustained engineering-best-practice contributions
Technologies & Skills:
Bachelor of Science (Computer Science)
2016 - 2020
Graduated with Bachelor of Science majoring in Computer Science
Honours thesis
Software-Level Implementation of Quantum Finite Automata
Coursework:
Exchange Studies (Computer Science)
2018 - 2018
Exchange semester studying advanced computer science courses
Coursework:
Bachelor of Commerce (Finance)
2011 - 2015
Bachelor of Commerce with major in finance
Coursework:
Bachelor of Science (Psychology)
2011 - 2015
Bachelor of Science with extended major in Psychology, specializing in applied psychology
Coursework: