Senior Machine Learning Engineer - Private Search Quality (AU remote)

Company Description 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.

Job Description

About the Group/Team We're the Discovery group, part of Canva's Content & Discovery supergroup. Our mission is to enable every user to effortlessly find content that empowers them to design. Within the group, the Private Search Quality team owns helping users find their own personal, team, and branded content — the search experience people rely on every time they go looking for something they've already made or been given access to.

About the Role/Specialty As a Machine Learning Engineer on Private Search Quality, you'll work on the ranking and relevance systems behind how Canva surfaces private, team, and brand content. That means building and improving ML ranking models and lifting relevance for all users, and helping the team's search results hold up as usage patterns and content types keep expanding, including newer surfaces like agent-driven search. It also means working with our Backend Engineers with our ElasticSearch and Vector Search databases. You'll take ownership of scoped projects end-to-end, from problem framing through to shipped, measurable impact on search quality. There is a strong emphasis on experimentation and iterating on learnings to get the best outcomes for our users.

What you’ll do (responsibilities)

• Build, evaluate, and improve ML ranking models for private, team, and brand content search
• Drive relevance and recall improvements, with a particular focus on enterprise user outcomes
• Work on intelligent result understanding, search recovery, and exact title match to help users navigate results
• Contribute to indexing with a variety of technologies including ElasticSeach/OpenSearch, Vector search and annotation
• Extend private and brand search quality and coverage into newer surfaces like agent search
• Investigate and resolve quality or relevance regressions in production search
• Communicate technical trade-offs clearly, including to non-technical stakeholders, and share knowledge across the team

What we're looking for: You've got hands-on experience building and shipping ML systems in production, ideally in search, ranking, LTR or relevance, not just training models in a notebook. You're comfortable defining and moving the metrics that matter (relevance, recall, ranking quality) rather than just chasing model accuracy in isolation. You can