About the role
Join
the team redefining how the world experiences design. Hey, g'day, mabuhay, kia ora,你好, hallo, vítejte! Thanks for stopping by. We know job hunting can be a little time consuming and you're probably keen to find out what's on offer, so we'll get straight to the point. Where and how you can work Our flagship campus is in Sydney, with a second campus in Melbourne and co-working spaces in Brisbane, Perth, & Adelaide. You have flexibility in how and where you work — whether that's from one of our spaces, from home, or a mix of both. This role is remote-friendly within Australia , so you can choose the setup that empowers you and your team to do your best work.
About
the Role/Specialty: As a Senior Machine Learning Engineer on Elements Platform, you'll build the ML systems that power how style gets understood — and how that understanding gets reflected in the elements generated for people's designs. Beyond that, you'll help shape how templates and elements get generated at scale. You'll work closely with the Content Engine, Core Research, and AI Media teams to take projects from a strong idea to a dependable, scaled system. This is a role for someone who wants to own real ML infrastructure — not just train models, but ship, monitor, and improve systems that directly shape what millions of people see when they open Canva. You'll have real autonomy to identify problems, propose solutions, and see them through to production. What you’ll do (
responsibilities): Design, build, and ship ML models that power content generation and quality eval scoring for Canva's generated element and template library. Own the full ML lifecycle — from data pipelines and training through to deployment, monitoring, and iteration. Partner with Content Engine, CORE AI Research, AI Media, and Discovery teams to align ML work with the broader content strategy. Diagnose and resolve issues across model performance, data quality, and production reliability. Run experiments to validate ML approaches before scaling them, with clear success metrics. Contribute to the engineering standards and tooling that make ML development faster and more reliable across the group. Communicate technical tradeoffs clearly to both engineering and non-engineering stakeholders.
