Senior Machine Learning Engineer
Job description
About the role
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
What you'll do
Generation methods are tiered across user segments, and workflows are shaped to deliver the right experience for each tier.
Issues in quality, latency, or reliability within the generation serving path are investigated and resolved to maintain a stable user experience.
Infrastructure and pipelines that move generation models into production are built and maintained to ensure reliable operation at Canva's scale.
Requirements
Hands-on experience building and shipping ML models in production is required, beyond training models only in a notebook.
Fluency across the full stack from model development to the infrastructure that serves predictions at scale is necessary, with care for tradeoffs between generation quality, speed, and reliability.
The ability to own scoped projects from idea through to measurable production impact with minimal guidance is required.
Comfort operating with autonomy to drive ideas to proof of concept without a fully predefined process is required.
Thrive in a fast, iterative pace where experiments move quickly, learnings are rapid, and pivots are accepted over long fixed research plans.
Collaboration with other engineers to create real user-facing products is required.
Practical notes
This role is based in Sydney and is a full-time position.
You must be eligible to work in Australia for this position.
Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
Roles focused on production ML involve shipping features that real users rely on daily.
Image and design generation systems combine research ideas with infrastructure to serve models at scale.
Cross-functional collaboration with product, engineering, and design teams is common.
Fast experimentation cycles help teams learn quickly and adjust direction.
Ownership and autonomy are valued in driving projects to completion.
Questions to ask
Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.
About the company
Canva is an online design and visual communication platform with a mission to empower everyone to design anything. Founded by Melanie Perkins, Cliff Obrecht, and Cameron Adams in 2013, Canva has over 190 million monthly active users and was valued at $26 billion in 2024.