Staff Machine Learning Engineer
Software EngineeringPosted 30 Jun 2026
Advertised as “Staff Machine Learning Engineer - Recommendations”
Posted 30 Jun 2026
Advertised as “Staff Machine Learning Engineer - Recommendations”
The original posting with the fluff stripped out
About the Group/Team:
Canva is full of AI-powered magic, but our ML-powered Recommendations is one of the highest-traffic machine-learning system at Canva. Serving over two hundred million users every month, our recommendations help Canva users to find exactly what they’re looking for before they even know it. We are always working with cutting-edge technology, reading new research papers, and running dramatic experiments to improve our recommender systems.
The Recommendations team is a great fit for someone looking to make a huge impact on Canva users by employing the latest in Machine Learning Research. A few of the Recommender Systems that we own:
Recommending Canva templates to users to get them started with their design, helping them jump-start their creative process.
“Magic Recommendations” for images, graphics, video and audio, enabling users to automatically find the perfect content to enhance their designs.
Design-aware recommendations for images, graphics inside the Canva Editor.
Recommendations for Canva Creators you might like to follow.
Recommendations for Fonts you might like to use.
And many more!
About the Role/Specialty:
At Canva, our Recommendations team is at the forefront of crafting personalized experiences that delight our users. As a Staff Machine Learning Engineer, you'll set the technical direction for our recommendation systems and lead the highest-impact bets that shape how Canva understands and anticipates user intent. We innovate by integrating the latest research into our recommendation algorithms, driving user engagement through highly relevant and timely suggestions. Your work will directly influence millions of users by enhancing their design journey, making your contributions pivotal to Canva’s success.
This role is perfect for someone with deep experience building production recommender systems at scale who is passionate about creating intuitive and effective user experiences. You will work closely with other engineers, data scientists, and product managers to drive innovation, raise the technical bar across the team, and improve the overall quality of our recommendations.
What you’ll do (responsibilities):
You'll design, implement, and refine machine learning models to deliver personalized recommendations, taking on the most complex and ambiguous parts of the system.
You'll improve the architecture, code structure, and performance of our machine learning systems, helping raise the engineering quality bar across the team.
You'll investigate research papers and state-of-the-art machine learning models, and judge which ones are worth bringing into production at Canva.
You'll lead the design of online and offline experiments to validate model performance, and make data-driven calls on what to ship and what to iterate on.
You'll shape how our machine learning models integrate with the broader technology stack, ensuring strong performance and reliability across the pipeline.
You'll work closely with product and engineering teams to deploy new recommendation features, driving alignment when initiatives span multiple teams.
You'll document and communicate your work to both technical and non-technical stakeholders — including senior leadership — and mentor MLEs around you through reviews, pairing, and shared knowledge.
What we're looking for:
We're looking for an experienced Machine Learning Engineer who has built and shipped recommendation systems at scale, and is excited to take on the most complex and ambiguous problems in personalization. You should be adept at designing and refining recommendation systems, comfortable working with large datasets, and skilled in both ML engineering and applied research.
Significant experience developing, shipping, and operating production-scale recommendation systems.
Proficiency in Python and core ML tooling (PyTorch, pandas, scikit-learn, numpy), with strong fluency on the engineering side of ML — training pipelines, evaluation, and serving.
Strong analytical skills, with a track record of rigorously evaluating model performance and making data-driven calls on what to ship and what to iterate on.
Excellent communication skills, with the ability to explain complex technical concepts to a wide range of audiences — including senior leadership.
A collaborative mindset and a passion for working with cross-functional teams to achieve shared goals, including supporting the growth of MLEs around you.
What will you learn and develop at Canva:
You'll work on one of the most heavily used ML systems at Canva, with direct visibility into the impact on hundreds of millions of users.
You'll have the scope to explore and apply cutting-edge machine learning techniques, and the autonomy to help shape which bets the team takes.
You'll partner with strong ML, engineering, and product peers across Canva, and grow as a technical leader through the breadth of problems you take on.
Don't tick all the boxes? Don't worry about that - nobody does!
We’d still love to hear from you! At Canva, we know that great engineers come from a variety of backgrounds, and we value passion, curiosity, and a willingness to learn just as much as specific experience. If you're excited about this role but don’t tick every box, we encourage you to apply, you might a great fit in ways you didn’t expect!
What's in it for you?
Achieving our crazy big goals motivates us to work hard - and we do - but you'll experience lots of moments of magic, connectivity and fun woven throughout life at Canva, too. We also offer a stack of benefits to set you up for every success in and outside of work.
Here's a taste of what's on offer:
Check out lifeatcanva.com for more info.
Other stuff to know
We make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture. When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.
All interviews are conducted virtually
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.